Experimental results on three cover song datasets verify the superiority of the proposed model , in terms of identification accuracy and classification accuracy , over state-of-the-art CSI schemes based on single similarity function or similarity fusion . The proposed model can be modified and applied in other tasks , such as image classification , visual object tracking , and drug taxonomy , etc . Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems . However , visual object tracking faces various challenging issues because tracking is influenced by illumination change , pose change , partial occlusion and background clutter . However , if the filters have infinite impulse response ( IIR ) structures , the visual trackers adopting them can exhibit degraded tracking performance when system models have parameter uncertainties or when the noise information is incorrect . To overcome this problem , in this paper , we propose a new finite impulse response ( FIR ) filter for visual object tracking ( VOT ) . The proposed filter is derived by maximizing the likelihood function , and it is referred to as the maximum likelihood FIR filter ( MLFIRF ) . Besides localization , spatial audio events can be detected /verified through various decision-making systems utilizing multichannel audio features . Visual object detection and tracking is also considered , either in parallel with the sound system or subsequently , after the initial sound localization . The case scenario examined in this paper consists of energy-based sound localization using a core cross-shaped coincident microphone array combined with state of the art machine vision , such as the OpenCV face detection pre-trained classifiers and the Open Tracking and Learning Detection ( openTLD ) framework . As such , it started to rapidly find its way into many commercial and industrial applications , with the disciplines of visual object detection and tracking being most prominent . A good example of computer vision being applied in practice , is the social network and website Facebook , where advanced object detection algorithms are utilized to detect and recognize people and in particular -- their faces . Advances in mathematical models and optimization introduce powerful tools for solving the visual object tracking problem , but this fundamental problem remains unsolved due to various challenges such as illumination variation , deformation and occlusion . Recent progress in correlation filter based models has provided an effective and efficient tracking solution . Visual object recognition and tracking require a good resolution of the object to accurately model its appearance . In addition , tracking systems must be able to robustly recover moving target trajectory , and possibly cope with fast motion and large displacements . The main motivation of this work is to show , for educational purposes , that the visual based object tracking problem can be illustrated through the simulation-in-the-loop approach : by using the MathWorks ™ Virtual Reality Toolbox together with Matlab ® , it is possible to simulate the behavior of a drone in a 3D environment when detection and control algorithms are run . Matlab VR is used due to the familiarity that students have with . In this research , we 've tried to develop a method with background subtraction , distance measurement and color histogram with particle filter , to track any single moving object . In visual moving object tracking , the appearance of both objects and the surrounding scenes may experience enormous variations due to changes in the scale and viewing angles , or partial occlusions . Also the objects and the backgrounds may have confusing color . Visual Object tracking research has undergone significant improvement in the past few years . The emergence of tracking by detection approach in tracking paradigm has been quite successful in many ways . In this section , we briefly cover most representative techniques for the two problems tackled in this paper . Visual object tracking . Arguably , until very recently , the most popular paradigm for tracking arbitrary objects has been to train online a discriminative classifier exclusively from the ground - truth information provided in the first frame of a video ( and then update it online ) . Abstract In intelligent video surveillance , the key issues are the real time and robustness in multi-object tracking . We proposed a mixed tracking algorithm based on codebook and particle filter in this article . In particular the segmentation and tracking of bacteria cells imaged in phase-contrast microscopy --- with error rates compatible with high-throughput data --- is a challenging problem . In this work , we describe a novel formulation of the multi-object tracking problem , in which tracking is performed by a regression of the bacteria 's displacement , allowing simultaneous tracking of multiple bacteria , despite their growth and division over time . Our method performs jointly segmentation and tracking , leveraging sequential information to increase segmentation accuracy . We investigate the application of learned convolutional filters in multi-object visual tracking . The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks . MCMC-based multi-object visual tracking was investigated here . To improve the confidence of sampling and perform the iteration effectively , a new approach to multi-object visual tracking was proposed based on reversible jump Markov chain Monte Carlo(RJMCMC ) sampling . Given image observation , the tracking problem was formulated as computing the MAP(maximum a posteriori ) estimation . In this contribution , we propose to use road and lane information as contextual cues in order to increase the precision of multi-object object tracking . For tracking , we employ a Monte Carlo implementation of a Probability Hypothesis Density (PHD)-filter , whereas scene context ( road and lane information ) is taken from annotated street maps . The first method performed well at a signal-to-noise ratio ( SNR ) of 15 dB or higher but was unsatisfactory below this . The second method was found to perform well at very low SNR approaching 0 dB. The paper discusses the multiobject tracking scenario , describes the association logic and PF implementations , discusses the analysis details , and shows the performance obtained from the PF . TViews is an extensible method and acoustic-based sensing framework for creating digital media tables that overcome many limitations of single-purpose systems . The system provides a means for real-time multiobject tracking on the tabletop and for the management of large numbers of objects and applications across multiple platform instances [ reference ] we introduce a general formulation for multi - target tracking that follows and allows us to define pose estimation and articulated tracking in a unified framework . We then describe the details of our articulated tracking approach in Sec . [ reference ] we introduced a general framework for multi - object tracking by solving an instance of the subgraph multicut problem . The subgraph multicut problem is NP - hard , but recent work has shown that efficient approximate inference is possible with local search methods . This was achieved implementing a new parallel and distributed architecture that allows a continuous and non-scanning control of each LED . A three level architecture separates the process of video generation , image loading and image displaying . The video generator is a PC multimedia system . Architectural changes in benchtop and ATE ( automatic test equipment ) hardware and software used for video signal generation have presented engineers with an opportunity to update and focus test techniques used in evaluating video signal processors . Four new test signal formats for general use in video testing are proposed : the circle signal for chrominance bandwidth analysis , the chirp and multitone signals for luminance bandwidth analysis , and the butterfly signal , a general-purpose RGB test signal for component evaluation of chroma demodulators . Generating image and video is a hot topic in Deep Learning . Especially , generating video is a difficult but meaningful work . How to generate video which has diversity and plausibility is still a problem to be solved . This paper presents Conditional Flow and Texture GAN ( CFT-GAN ) , a GAN-based video generation method from action-appearance captions . We propose a novel way of generating video by encoding a caption ( e.g. , " a man in blue jeans is playing golf " ) in a two-stage generation pipeline . Our CFT-GAN uses such caption to generate an optical flow ( action ) and a texture ( appearance ) for each frame . This chapter considers the nature and implications of indexicality at a time when video capturing capabilities have become increasingly ubiquitous , and embedded in mobile , locative , and networked devices where image and sound recording operate together with an array of other sensors . The generation of video together with metadata involves the generation of additional layers of information which carry meaning above and beyond that seen within the frame . At one level , metadata help to augment the “ sense-making ” capability of documentary makers , increasing the means to scale up workflows around the collation , organization , and sifting of material . In this paper , we study delivery of real 3D video using the state-of-the-art compression technology . Considered are the issues related to 3D video data model as well its application for generation of video from arbitrary virtual viewpoint . Included are respective experimental results . With the current rate of video data generation , there is an urgent need of automatic video content analysis for subsequent purposes such as summarization , retrieval and classification . And video shot boundary detection is usually the first step to segment a video clip into meaningful shots . Furthermore , we tackle the Gordian Knot in training GANs - the performance control and training stability by experimenting with using Wasserstein distance ( gradient clipping , penalty ) and Spectral Normalization . The experimental findings of this paper should propel the application of capsules and GANs in the still exciting and nascent domain of 3D image generation , and plausibly video ( frame ) generation . We developed a methodology to automatically organize video material in an edited video sequence with a rhetorical structure . This was enabled by defining an annotation schema for the material and a genera- tion process with the following two requirements : • the data repository used by the generation process could be extended by simply adding annotated material to it • the final resulting structure of the video generation process would seem familiar to a video literate user The first requirement was satisfied by developing an annotation schema that explic- itly identifies rhetorical elements in the video material , and a generation process that assembles longer sequences of video by manipulating the annotations in a bottom-up fashion . The second requirement was satisfied by modelling the generation process accord- ing to documentary making and general film theory techniques , in particular making the role of rhetoric in video documentaries explicit . The resulting information is aggregated in an undistorted image view of the entire field that is very interesting and meaningful to the target end-user . The generation of the video is a demanding computational task that takes advantage of using parallel computing . The resulting videos are interesting and include several important informations to the human end-user . Spatiotemporal LSTMs have shown promise in the area of real-world video prediction . Building on this success , we approach this physics emulation problem as a video generation problem : training the model on simulation data to learn the underlying dynamic behavior . Our novel deep learning model is a Physics-Informed Spatiotemporal LSTM , that uses modified loss functions and partial derivatives from the stress field to build a data-driven coupled dynamics emulator . This is achieved by domain-specific pre-disentangled structure representation , partially shared domain encoder layers , and a structure discriminator . We also propose a simple yet effective temporal conditioning method to enforce consistency for video sequence generation . We demonstrate the superiority of our method by testing it on large amount of portraits , and comparing with alternative baselines and state-of-the-art unsupervised image translation methods . In this work we have investigated existing knowledge distillation methods for NMT ( which work at the word - level ) and introduced two sequence - level variants of knowledge distillation , which provide improvements over standard word - level knowledge distillation . We have chosen to focus on translation as this domain has generally required the largest capacity deep learning models , but the sequence - to - sequence framework has been successfully applied to a wide range of tasks including parsing , summarization , dialogue , NER / POS - tagging , image captioning , video generation , and speech recognition . We anticipate that methods described in this paper can be used to similarly train smaller models in other domains . For example , when trained on movie subtitles , these language models are able to generate basic answers to questions about object colors , facts about people , etc . Lastly , recently proposed sequence - to - sequence models employ conditional language models as their key component to solve diverse tasks like machine translation or video generation . Deep Learning and Recurrent Neural Networks ( RNNs ) have fueled language modeling research in the past years as it allowed researchers to explore many tasks for which the strong conditional independence assumptions are unrealistic . We present a data store managing large videos for retrospective analytics . Our data store orchestrates video ingestion , storage , retrieval , and consumption . Towards resource efficiency , it takes the key opportunity of controlling the video formats along the video data path . We present VStore , a data store for supporting fast , resource-efficient analytics over large archival videos . VStore manages video ingestion , storage , retrieval , and consumption . It controls video formats along the video data path . Rate-distortion optimization can significantly improve encoder performance in MPEG-like video coding applications . Nevertheless , mode selection and motion estimation ( ME ) in a rate-distortion optimized setting can entail a prohibitive computational cost . The object of this invention is to provide a method and device for using blue tooth and off-line compression technology to realoze radio transmission real time video picture , radi oremote control picture quality , free selection of monitor objects including : video signal from the video channel converts to data flow via picture collection end , real time compressed code/decode chip carries out smallwave compressed coding to be sent to the blue tooth module by the blue tooth to be recovered to video analog signal and display by a monitor after decompressed by real time code/decode chip and codedby video output coder . The reported software can process 4 Megapixel 8-bit raw frames from a sensor array acquired at a sustained rate of 80 Hz . Video-rate image rendering is achieved by streamline image processing with commercial computer graphics hardware . For time-averaged holograms acquired in off-axis optical configuration with a frequency-shifted reference beam , wide-field imaging of one tunable spectral component is permitted . We embed a “ sparse-filtering ” stage into the measurement matrix by weighting down the contribution of signal coefficients that are outside the support estimate . We present an application which can benefit from the proposed sampling scheme , namely , video compressive acquisition . We demonstrate that our proposed adaptive CS scheme results in a significant improvement in reconstruction quality compared with standard CS as well as adaptive recovery using weighted ℓ1 minimization . MPEG ( Motion Picture Experts Group ) is a compression and communications protocol which defines a syntax for transmitting several data types , including audio , user-data , and full-motion compressed video . MPEG allows ` tolerable ' NTSC full-motion video transmission at data rates as low as 1.2 Mbps , and video-conference-quality transmission at rates as low as 56 Kbps . However , since the MPEG standard includes JPEG as a subset , it allows the transmission of compressed still images as well . Advanced systems for finding , using , sharing , and remixing video require high-level representations of video content . A number of researchers have taken top-down , analytic approaches to the specification of representation structures for video . DESCRIPTION OF INTERVENTION The study was exploratory in order to capture and describe the construction of scientific knowledge that took place . DESIGN AND METHODS We utilized a multiple case study design where data was collected in the form of : the products that the two participants created , video recording of the construction process , pre- and post- interviews and a collection of artefacts of interest , such as images of the creation process/set up . RESULTS Analysis of the knowledge represented across the various data sources for the two cases show that they differ in degrees of coherence and abstraction profiles for the scientific concept represented : transparency . Three studies of video conferencing are reported . The first describes a video conferencing facility linking two locations about 35 miles apart , and summarizes the reactions of people who used it for naturally occurring conferences . This facility used conventional television cameras and monitors , and incorporated voice-activated switching among cameras to provide “ close-up ” video coverage of the person talking at any given time . This visualization course for design foundations is organized to maximize the use of games as a medium and storytelling as a vehicle to contextualize content and articulate vision . More than a decade of experience in the 3D animation and video game industries informed the instructors about how to make the most of what these types of practices have to offer beginning design students . Experimental results show that the proposed method outperforms many state - of - the - art ones . As future work we plan to explore MSSRNet for video processing . section : ACKNOWLEDGEMENTS Tracking is a fundamental task in any video application requiring some degree of reasoning about objects of interest , as it allows to establish object correspondances between frames [ reference ] . It finds use in a wide range of scenarios such as automatic surveillance , vehicle navigation , video labelling , human - computer interaction and activity recognition . Given the location of an arbitrary target of interest in the first frame of a video , the aim of visual object tracking is to estimate its position in all the subsequent frames [ reference ][ reference ][ reference ] . However , in this case the object representation consists of a binary segmentation mask which expresses whether each pixel belongs to the target or not [ reference ] . Such a detailed representation is more desirable for applications that require pixel - level information , like video editing [ reference ] and rotoscoping [ reference ] . Understandably , producing pixel - level estimates requires more computational re - sources than a simple bounding box . As a consequence , VOS methods have been traditionally slow , often requiring several seconds per frame ( e.g. [ reference ][ reference ][ reference ][ reference ] ) . These methods greatly raise efficiency with notably sacrificed accuracy . subsubsection : Video Semantic Segmentation Videos contain redundant information in frames , which can be utilized to reduce computation .
Experimental results on three cover song datasets verify the superiority of the proposed model , in terms of identification accuracy and classification accuracy , over state-of-the-art CSI schemes based on single similarity function or similarity fusion . The proposed model can be modified and applied in other tasks , such as image classification , visual object tracking , and drug taxonomy , etc .
Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems . However , visual object tracking faces various challenging issues because tracking is influenced by illumination change , pose change , partial occlusion and background clutter .
However , if the filters have infinite impulse response ( IIR ) structures , the visual trackers adopting them can exhibit degraded tracking performance when system models have parameter uncertainties or when the noise information is incorrect . To overcome this problem , in this paper , we propose a new finite impulse response ( FIR ) filter for visual object tracking ( VOT ) . The proposed filter is derived by maximizing the likelihood function , and it is referred to as the maximum likelihood FIR filter ( MLFIRF ) .
Besides localization , spatial audio events can be detected /verified through various decision-making systems utilizing multichannel audio features . Visual object detection and tracking is also considered , either in parallel with the sound system or subsequently , after the initial sound localization . The case scenario examined in this paper consists of energy-based sound localization using a core cross-shaped coincident microphone array combined with state of the art machine vision , such as the OpenCV face detection pre-trained classifiers and the Open Tracking and Learning Detection ( openTLD ) framework .
As such , it started to rapidly find its way into many commercial and industrial applications , with the disciplines of visual object detection and tracking being most prominent . A good example of computer vision being applied in practice , is the social network and website Facebook , where advanced object detection algorithms are utilized to detect and recognize people and in particular -- their faces .
Advances in mathematical models and optimization introduce powerful tools for solving the visual object tracking problem , but this fundamental problem remains unsolved due to various challenges such as illumination variation , deformation and occlusion . Recent progress in correlation filter based models has provided an effective and efficient tracking solution .
Visual object recognition and tracking require a good resolution of the object to accurately model its appearance . In addition , tracking systems must be able to robustly recover moving target trajectory , and possibly cope with fast motion and large displacements .
The main motivation of this work is to show , for educational purposes , that the visual based object tracking problem can be illustrated through the simulation-in-the-loop approach : by using the MathWorks ™ Virtual Reality Toolbox together with Matlab ® , it is possible to simulate the behavior of a drone in a 3D environment when detection and control algorithms are run . Matlab VR is used due to the familiarity that students have with .
In this research , we 've tried to develop a method with background subtraction , distance measurement and color histogram with particle filter , to track any single moving object . In visual moving object tracking , the appearance of both objects and the surrounding scenes may experience enormous variations due to changes in the scale and viewing angles , or partial occlusions . Also the objects and the backgrounds may have confusing color .
Visual Object tracking research has undergone significant improvement in the past few years . The emergence of tracking by detection approach in tracking paradigm has been quite successful in many ways .
In this section , we briefly cover most representative techniques for the two problems tackled in this paper . Visual object tracking . Arguably , until very recently , the most popular paradigm for tracking arbitrary objects has been to train online a discriminative classifier exclusively from the ground - truth information provided in the first frame of a video ( and then update it online ) .
Abstract In intelligent video surveillance , the key issues are the real time and robustness in multi-object tracking . We proposed a mixed tracking algorithm based on codebook and particle filter in this article .
In particular the segmentation and tracking of bacteria cells imaged in phase-contrast microscopy --- with error rates compatible with high-throughput data --- is a challenging problem . In this work , we describe a novel formulation of the multi-object tracking problem , in which tracking is performed by a regression of the bacteria 's displacement , allowing simultaneous tracking of multiple bacteria , despite their growth and division over time . Our method performs jointly segmentation and tracking , leveraging sequential information to increase segmentation accuracy .
We investigate the application of learned convolutional filters in multi-object visual tracking . The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks .
MCMC-based multi-object visual tracking was investigated here . To improve the confidence of sampling and perform the iteration effectively , a new approach to multi-object visual tracking was proposed based on reversible jump Markov chain Monte Carlo(RJMCMC ) sampling . Given image observation , the tracking problem was formulated as computing the MAP(maximum a posteriori ) estimation .
In this contribution , we propose to use road and lane information as contextual cues in order to increase the precision of multi-object object tracking . For tracking , we employ a Monte Carlo implementation of a Probability Hypothesis Density (PHD)-filter , whereas scene context ( road and lane information ) is taken from annotated street maps .
The first method performed well at a signal-to-noise ratio ( SNR ) of 15 dB or higher but was unsatisfactory below this . The second method was found to perform well at very low SNR approaching 0 dB. The paper discusses the multiobject tracking scenario , describes the association logic and PF implementations , discusses the analysis details , and shows the performance obtained from the PF .
TViews is an extensible method and acoustic-based sensing framework for creating digital media tables that overcome many limitations of single-purpose systems . The system provides a means for real-time multiobject tracking on the tabletop and for the management of large numbers of objects and applications across multiple platform instances
[ reference ] we introduce a general formulation for multi - target tracking that follows and allows us to define pose estimation and articulated tracking in a unified framework . We then describe the details of our articulated tracking approach in Sec .
[ reference ] we introduced a general framework for multi - object tracking by solving an instance of the subgraph multicut problem . The subgraph multicut problem is NP - hard , but recent work has shown that efficient approximate inference is possible with local search methods .
This was achieved implementing a new parallel and distributed architecture that allows a continuous and non-scanning control of each LED . A three level architecture separates the process of video generation , image loading and image displaying . The video generator is a PC multimedia system .
Architectural changes in benchtop and ATE ( automatic test equipment ) hardware and software used for video signal generation have presented engineers with an opportunity to update and focus test techniques used in evaluating video signal processors . Four new test signal formats for general use in video testing are proposed : the circle signal for chrominance bandwidth analysis , the chirp and multitone signals for luminance bandwidth analysis , and the butterfly signal , a general-purpose RGB test signal for component evaluation of chroma demodulators .
Generating image and video is a hot topic in Deep Learning . Especially , generating video is a difficult but meaningful work . How to generate video which has diversity and plausibility is still a problem to be solved .
This paper presents Conditional Flow and Texture GAN ( CFT-GAN ) , a GAN-based video generation method from action-appearance captions . We propose a novel way of generating video by encoding a caption ( e.g. , " a man in blue jeans is playing golf " ) in a two-stage generation pipeline . Our CFT-GAN uses such caption to generate an optical flow ( action ) and a texture ( appearance ) for each frame .
This chapter considers the nature and implications of indexicality at a time when video capturing capabilities have become increasingly ubiquitous , and embedded in mobile , locative , and networked devices where image and sound recording operate together with an array of other sensors . The generation of video together with metadata involves the generation of additional layers of information which carry meaning above and beyond that seen within the frame . At one level , metadata help to augment the “ sense-making ” capability of documentary makers , increasing the means to scale up workflows around the collation , organization , and sifting of material .
In this paper , we study delivery of real 3D video using the state-of-the-art compression technology . Considered are the issues related to 3D video data model as well its application for generation of video from arbitrary virtual viewpoint . Included are respective experimental results .
With the current rate of video data generation , there is an urgent need of automatic video content analysis for subsequent purposes such as summarization , retrieval and classification . And video shot boundary detection is usually the first step to segment a video clip into meaningful shots .
Furthermore , we tackle the Gordian Knot in training GANs - the performance control and training stability by experimenting with using Wasserstein distance ( gradient clipping , penalty ) and Spectral Normalization . The experimental findings of this paper should propel the application of capsules and GANs in the still exciting and nascent domain of 3D image generation , and plausibly video ( frame ) generation .
We developed a methodology to automatically organize video material in an edited video sequence with a rhetorical structure . This was enabled by defining an annotation schema for the material and a genera- tion process with the following two requirements : • the data repository used by the generation process could be extended by simply adding annotated material to it • the final resulting structure of the video generation process would seem familiar to a video literate user The first requirement was satisfied by developing an annotation schema that explic- itly identifies rhetorical elements in the video material , and a generation process that assembles longer sequences of video by manipulating the annotations in a bottom-up fashion . The second requirement was satisfied by modelling the generation process accord- ing to documentary making and general film theory techniques , in particular making the role of rhetoric in video documentaries explicit .
The resulting information is aggregated in an undistorted image view of the entire field that is very interesting and meaningful to the target end-user . The generation of the video is a demanding computational task that takes advantage of using parallel computing . The resulting videos are interesting and include several important informations to the human end-user .
Spatiotemporal LSTMs have shown promise in the area of real-world video prediction . Building on this success , we approach this physics emulation problem as a video generation problem : training the model on simulation data to learn the underlying dynamic behavior . Our novel deep learning model is a Physics-Informed Spatiotemporal LSTM , that uses modified loss functions and partial derivatives from the stress field to build a data-driven coupled dynamics emulator .
This is achieved by domain-specific pre-disentangled structure representation , partially shared domain encoder layers , and a structure discriminator . We also propose a simple yet effective temporal conditioning method to enforce consistency for video sequence generation . We demonstrate the superiority of our method by testing it on large amount of portraits , and comparing with alternative baselines and state-of-the-art unsupervised image translation methods .
In this work we have investigated existing knowledge distillation methods for NMT ( which work at the word - level ) and introduced two sequence - level variants of knowledge distillation , which provide improvements over standard word - level knowledge distillation . We have chosen to focus on translation as this domain has generally required the largest capacity deep learning models , but the sequence - to - sequence framework has been successfully applied to a wide range of tasks including parsing , summarization , dialogue , NER / POS - tagging , image captioning , video generation , and speech recognition . We anticipate that methods described in this paper can be used to similarly train smaller models in other domains .
For example , when trained on movie subtitles , these language models are able to generate basic answers to questions about object colors , facts about people , etc . Lastly , recently proposed sequence - to - sequence models employ conditional language models as their key component to solve diverse tasks like machine translation or video generation . Deep Learning and Recurrent Neural Networks ( RNNs ) have fueled language modeling research in the past years as it allowed researchers to explore many tasks for which the strong conditional independence assumptions are unrealistic .
We present a data store managing large videos for retrospective analytics . Our data store orchestrates video ingestion , storage , retrieval , and consumption . Towards resource efficiency , it takes the key opportunity of controlling the video formats along the video data path .
We present VStore , a data store for supporting fast , resource-efficient analytics over large archival videos . VStore manages video ingestion , storage , retrieval , and consumption . It controls video formats along the video data path .
Rate-distortion optimization can significantly improve encoder performance in MPEG-like video coding applications . Nevertheless , mode selection and motion estimation ( ME ) in a rate-distortion optimized setting can entail a prohibitive computational cost .
The object of this invention is to provide a method and device for using blue tooth and off-line compression technology to realoze radio transmission real time video picture , radi oremote control picture quality , free selection of monitor objects including : video signal from the video channel converts to data flow via picture collection end , real time compressed code/decode chip carries out smallwave compressed coding to be sent to the blue tooth module by the blue tooth to be recovered to video analog signal and display by a monitor after decompressed by real time code/decode chip and codedby video output coder .
The reported software can process 4 Megapixel 8-bit raw frames from a sensor array acquired at a sustained rate of 80 Hz . Video-rate image rendering is achieved by streamline image processing with commercial computer graphics hardware . For time-averaged holograms acquired in off-axis optical configuration with a frequency-shifted reference beam , wide-field imaging of one tunable spectral component is permitted .
We embed a “ sparse-filtering ” stage into the measurement matrix by weighting down the contribution of signal coefficients that are outside the support estimate . We present an application which can benefit from the proposed sampling scheme , namely , video compressive acquisition . We demonstrate that our proposed adaptive CS scheme results in a significant improvement in reconstruction quality compared with standard CS as well as adaptive recovery using weighted ℓ1 minimization .
MPEG ( Motion Picture Experts Group ) is a compression and communications protocol which defines a syntax for transmitting several data types , including audio , user-data , and full-motion compressed video . MPEG allows ` tolerable ' NTSC full-motion video transmission at data rates as low as 1.2 Mbps , and video-conference-quality transmission at rates as low as 56 Kbps . However , since the MPEG standard includes JPEG as a subset , it allows the transmission of compressed still images as well .
Advanced systems for finding , using , sharing , and remixing video require high-level representations of video content . A number of researchers have taken top-down , analytic approaches to the specification of representation structures for video .
DESCRIPTION OF INTERVENTION The study was exploratory in order to capture and describe the construction of scientific knowledge that took place . DESIGN AND METHODS We utilized a multiple case study design where data was collected in the form of : the products that the two participants created , video recording of the construction process , pre- and post- interviews and a collection of artefacts of interest , such as images of the creation process/set up . RESULTS Analysis of the knowledge represented across the various data sources for the two cases show that they differ in degrees of coherence and abstraction profiles for the scientific concept represented : transparency .
Three studies of video conferencing are reported . The first describes a video conferencing facility linking two locations about 35 miles apart , and summarizes the reactions of people who used it for naturally occurring conferences . This facility used conventional television cameras and monitors , and incorporated voice-activated switching among cameras to provide “ close-up ” video coverage of the person talking at any given time .
This visualization course for design foundations is organized to maximize the use of games as a medium and storytelling as a vehicle to contextualize content and articulate vision . More than a decade of experience in the 3D animation and video game industries informed the instructors about how to make the most of what these types of practices have to offer beginning design students .
Experimental results show that the proposed method outperforms many state - of - the - art ones . As future work we plan to explore MSSRNet for video processing . section : ACKNOWLEDGEMENTS
Tracking is a fundamental task in any video application requiring some degree of reasoning about objects of interest , as it allows to establish object correspondances between frames [ reference ] . It finds use in a wide range of scenarios such as automatic surveillance , vehicle navigation , video labelling , human - computer interaction and activity recognition . Given the location of an arbitrary target of interest in the first frame of a video , the aim of visual object tracking is to estimate its position in all the subsequent frames [ reference ][ reference ][ reference ] .
However , in this case the object representation consists of a binary segmentation mask which expresses whether each pixel belongs to the target or not [ reference ] . Such a detailed representation is more desirable for applications that require pixel - level information , like video editing [ reference ] and rotoscoping [ reference ] . Understandably , producing pixel - level estimates requires more computational re - sources than a simple bounding box . As a consequence , VOS methods have been traditionally slow , often requiring several seconds per frame ( e.g. [ reference ][ reference ][ reference ][ reference ] ) .
These methods greatly raise efficiency with notably sacrificed accuracy . subsubsection : Video Semantic Segmentation Videos contain redundant information in frames , which can be utilized to reduce computation .
Music similarity model based on CRP fusion and Multi-Kernel Integration Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning Maximum likelihood FIR filter for visual object tracking Investigating Multimodal Audiovisual Event Detection and Localization Implementation and Analysis of Real-time Object Tracking on the Starburst MPSoC Coupling deep correlation filter and online discriminative learning for visual object tracking Automatic Calibration of Hybrid Dynamic Vision System for High Resolution Object Tracking An educational simulation platform for GPS-denied unmanned Aerial Vehicles aimed to the detection and tracking of moving objects Particle filter based moving object tracking with adaptive observation model Rotation Adaptive Visual Object Tracking with Motion Consistency Fast Online Object Tracking and Segmentation: A Unifying Approach Moving Object Tracking Based on Codebook and Particle Filter DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine Learned filters for object detection in multi-object visual tracking Multi-object Visual Tracking Based on Reversible Jump Markov Chain Monte Carlo Context-based vector fields for multi-object tracking in application to road traffic Comparison of Two Particle Filter Methods For Image Plane Tracking of Multiple Objects TViews: An Extensible Architecture for Multiuser Digital Media Tables ArtTrack: Articulated Multi-Person Tracking in the Wild New full-colour LED based screen New video test signals offer higher quality testing of video signal processors Frame Difference Generative Adversarial Networks: Clearer Contour Video Generating Conditional Video Generation Using Action-Appearance Captions Indexicality in the Age of the Sensor and Metadata Efficient Transmission of 3D Video Using MPEG-4 AVC/H.264 Compression Technology A novel metric for efficient video shot boundary detection CapsGAN: Using Dynamic Routing for Generative Adversarial Networks Vox Populi : generating video documentaries from semantically annotated media repositories Tracking Players in Indoor Sports Using a Vision System Inspired in Fuzzy and Parallel Processing Physics-Informed Spatiotemporal Deep Learning for Emulating Coupled Dynamical Systems Neural Hair Rendering Sequence-Level Knowledge Distillation Exploring the Limits of Language Modeling Reinventing Data Stores for Video Analytics VStore: A Data Store for Analytics on Large Videos Low-complexity rate-distortion optimal macroblock mode selection and motion estimation for MPEG-like video coders Real-time compressed image and video transmitting method and device based on blue-tooth technology Video-rate computational heterodyne holography. Adaptive compressed sensing for video acquisition Transmitting and controlling images from small satellites using MPEG image compression Toward emergent representations for video Built during construction of a multimodal product Video Conferencing: Reactions of Users1 Games and Storytelling for Design Foundations Single image super-resolution with dilated convolution based multi-scale information learning inception module Fast Online Object Tracking and Segmentation: A Unifying Approach ICNet for Real-Time Semantic Segmentation on High-Resolution Images
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2019 2018 2016 2016 2015 2018 2008 2016 2017 2017 2019 2012 2020 2016 2012 2014 2013 2006 2017 2006 1990 2019 2018 2018 2010 2014 2019 2006 2012 2020 2020 2016 2016 2018 2019 2005 2002 2014 2012 1993 2005 2019 1973 2018 2017 2019 2018
Audio Mostly Conference CCA 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ITSC 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CANDAR Workshops FMN VCIP AAAI Spring Symposium: MLPS EMNLP ArXiv EuroSys ICASSP ACM Multimedia 2017 IEEE International Conference on Image Processing (ICIP) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ECCV
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