A new approach to block-based motion estimation for video compression , called the genetic motion search ( GMS ) algorithm , is introduced . It makes use of a natural processing concept called genetic algorithm ( GA ) . This paper presents a new kind of reference frame , i.e. , the " background frame " , to improve the accuracy of motion compensation in video compression . When an object moves in the scene of a video sequence , a certain part of the background is overlapped at first . PURPOSE : A motion estimator for video data compression is provided to carry out motion estimation according to the most suitable algorithm selected by a user . CONSTITUTION : A motion estimator(20 ) for video data compression includes a demultiplexer(210 ) , a motion estimating unit(200 ) , and a multiplexer(230 ) . Form the beginning of contrasting wavelet transformation with other transformation used for video compression coding , this paper expounds the advantages of wavelet transformation and introduces it 's theories and characteristics . Then the paper proposes two kinds of technologies for wavelet transforming coding , one is a coding technology based on zerotree , the other is a coding technology based on information source partition and bit plane . The invention discloses a video inter-frame compression coding method based on macroblock features and statistical properties and relates to the field of video compression coding . The method comprisesthe following steps : firstly extracting brightness information of the current coding macroblock from original video data , adopting the dual-layer prejudgment criteria for determining the optimal inter-frame coding mode according to the statistical properties of the optimal inter-frame mode selected by the current coding macroblock , further utilizing the time-space relevant features of the currentcoding macroblock for selectively carrying out intra-frame coding , comparing the optimal inter-frame coding mode with the optimal intra-frame coding mode according to a rate distortion cost function , determining the final inter-frame coding mode and coding the current macroblock . Distinctive features and content include using the Internet to illustrate how network protocols are designed ; state-of-the-art material on implementation and performance ( TCP congestion control , ATM networks , real-time Internet services and RSVP , routing protocols from RIP to BGP-4 and classless routing ( CIDR ) , Next Generation Internet Protocol ( IPv6 ) , network security , and video and video compression ( MPEG ) . The book employs code fragments from a working protocol environment , the x-kernel , to demonstrate how protocol specifications and design principles are implemented . The invention provides a decoding method based on edge intensity factors for conducting the de-blocking filter in the video compression processing , wherein , the acquisition of edge intensity factors is finished before the acquisition of the predication macro-block data and the residual block data . The method has the advantages that the current macro-block information can be released from a memory area in advance , and the needed information for the reference to macro blocks share the information existed in other storage area , so that the on-chip memory space in the decoding device is not exclusively occupied , and the memory space is in no need of being externally connected . At last , three search points is used to replace the small diamond search which in this paper is just a jump object for the most satisfied early-termination . Simulations shows that improved algorithm has a better performance in the terms of reducing motion estimation time , improving the speed of video coding compression and guaranteeing the quality of reconstructed image , comparing with UMHexagonS algorithm . Firstly , on the basis of analysis forvideo compression standard H.264/AVC , DSP TMS320C6416 and ARMmicrocontrollerare used to implement a flexible compressed platform with high quantity embedded digital video . Then the principle of system structure , hardware designand encodingalgorithm in are illustrateddetails . Finally , it is satisfying to note that the fitting function effectively represents the patch-wise geometry information . Moreover , new depth maps are obtained with extremely small and stable values , which are more suitable for video-based compression . Different patch-wise fitting parameters are preserved and coded using lossless compression through the open source PAQ project . The Discrete Cosine Transform ( DCT ) is one of the basic varieties of transform coding algorithms . Moreover DCT is used in standard algorithms of compression of still image ( JPEG ) and video compression of algorithms ( MPEG , H.26x ) . In case of discrete cosine transform of compression 's images of algorithms there are used the blocs : 8x8 pixels . From the Publisher : The Complete Guide to Video Compression Video compression is the magic behind digital TV , videoconferencing , DVDs , and Internet video . In Video Compression Demystified , specialist Peter Symes paints the full video compression picture in a friendly , not-too-big , not-too-technical introduction that explains what happens in compression , why compression works , and what will stop it from working . With the proposed video motion representation , two new motion features for video motion content indexing are proposed : 1 ) motion histogram and 2 ) motion correlogram . We demonstrate the usefulness of the motion features with three applications : ( i ) video retrieval , ( ii ) video clustering and ( iii ) video classification . With the PCRM based video motion representation , a new motion based key frame extraction method is developed . However , it is very difficult to obtain satisfactory classification accuracy due to traditional methods can not fully exploit multimodal information . Herein , we propose a novel approach to modeling cognitive events from EEG data by reducing it to a video classification problem , which is designed to preserve the multimodal information of EEG . In addition , optical flow is introduced to represent the variant information of EEG . However , the train-test discrepancy of action localization strategy prevents WTAL models from leveraging semi-supervision for further improvement . At training time , attention or multiple instance learning is used to aggregate predictions of each snippet for video-level classification ; at test time , they first obtain action score sequences over time , then truncate segments of scores higher than a fixed threshold , and post-process action segments . The inconsistent strategy makes it hard to explicitly supervise the action localization model with temporal boundary annotations at training time . Specifically , a frame-level preprocessing strategy is first proposed to efficiently decode the videos for useful information . Then an image-based model is developed to achieve the final video-level classification . For the multi-label classification case , three modules are proposed to enhance the model ’s classification capability , relieve the label-imbalance issue and exploit the label correlation respectively . We define Unequal Secure Encryption ( USE ) as an approach that applies different encryption schemes ( with different security strength ) to different parts of compressed video data . This USE scheme includes two parts : video data classification and unequal secure video data encryption . Firstly , we classify the video data into two partitions : Important data partition and unimportant data partition . In the past few years , the demand for real-time hardware implementations of deep neural networks ( DNNs ) , especially convolutional neural networks ( CNNs ) , has dramatically increased , thanks to their excellent performance on a wide range of recognition and classification tasks . When considering real-time action recognition and video/image classification systems , latency is of paramount importance . Therefore , applications strive to maximize the accuracy while keeping the latency under a given application-specific maximum : in most cases , this threshold can not exceed a few hundred milliseconds . MARVIN allows for the creation of Docker containers that run on Kubernetes clusters within DARPA to provide an execution environment for automated machine learning . MARVIN currently contains over 400 datasets and challenge problems from a wide array of ML domains including routine classification and regression to advanced video/image classification and remote sensing . The normal and disordered people balance ability classification is a key premise for rehabilitation training . This paper proposes a multi-barycentric area model ( MBAM ) , which can be applied for accurate video analysis based classification . First , we have invited fifty-three subjects to wear an HTC ( High Tech Computer Corporation ) VIVE ( Very Immersive Virtual Experience ) helmet and to walk ten meters while seeing a virtual environment . We present a real-time multisensor architecture for combined laser scanner and infra-red video-based pedestrian detection and tracking used within a road side unit for intersection assistance . In order to achieve outmost classification performance we propose a cascaded classifier using laser scanner hypothesis generation and histogram of oriented gradients ( HOG ) descriptors for video-based classification together with linear and Gaussian kernel support vector machines ( SVM ) . The entire classification cascade is implemented on a graphics processing unit ( GPU ) . Because of its multimodal characteristics , we decide to call this method Deep Multimodal Learning ( DML ) . Our DML-based model was trained on Google Cloud and our own server and was tested in a well-known video classification competition on Kaggle held by Google . The method comprises : conducting segmentation on a video in a sample video library in accordance with a time sequence , so as to obtain a segmentation result and generate a motion atom set ; by using the motion atom set and the segmentation result , generating a motion phrase set which can express a complex motion mode , and generating a description vector of the video in the sample video library based on the motion phrase set ; and by using the description vector , determining a video to be detected which is of the same type as the video in the sample video library . The method is suitable for a video classification scenario . The TOMB/P filter [ Williams , 2011 , 2015 ] is an attractive method for multiobject tracking . However , its original formulation is computationally feasible only for linear-Gaussian system models , and it suffers from the track coalescence effect . The results of this heuristic search are combined with the more common views of target objects , and intensity information in order to update the templates . As a result , the updating process will be computed only when the target object has evolved to a transformed shape dissimilar with respect to the current shape , providing robust tracking , and multiobject tracking because accurate template updating is performed . The paper includes experimental results with inside and outside video streams demonstrating the effectiveness and efficiency for real-time machine vision based tasks in unrestricted environments . Abstract In this paper , we address the problem of online multi-object tracking based on the Maximum a Posteriori ( MAP ) framework . Given the observations up to the current frame , we estimate the optimal object trajectories via two MAP estimation stages : object detection and data association . We first define the tracklet confidence using the detectability and continuity of a tracklet , and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence . We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values . Based on this strategy , tracklets sequentially grow with online-provided detections , and fragmented tracklets are linked up with others without any iterative and expensive association steps . Tracking-by-detection is a common approach for online multi-object tracking problem . At present , the following challenges still exist in the multi-object tracking scenarios : ( 1 ) The result of object re-tracking after full occlusion is not ideal ; ( 2 ) The predicted position of object is not accurate enough in the complicated video scenarios . A kernel-based clustering algorithm is proposed to group the projected points according to their height values and locations on the plane . By producing clusters , the number , position , and orientation of objects in the surveillance scene can be determined for online multi-object detection and tracking . Experiments on both indoor and outdoor applications with complex scenes show the advantages of the proposed system . There is a big challenge in online multi-object tracking-by-detection , which caused by frequent occlusions , false alarms or miss detections and other factors . In this paper , we proposed an improved fast online multi-object tracking method through taking into account the results of multiple single-object trackers and detections synthetically . The paper presents a novel method for online tracking of multiple objects with non-overlapping cameras . The method is based on a generative model defining probabilistic dependencies between observations , the underlying color properties of objects and their dynamics . Abstract In this paper , a novel fuzzy logic data association algorithm is proposed for online visual multi-object tracking . Firstly , in the proposed algorithm , in order to incorporate expert experience into the data association for the improvement of performance in multi-object tracking , a fuzzy inference system based on knowledge is designed by using a set of fuzzy if-then rules . Global optimization algorithms have shown impressive performance in data-association-based multi-object tracking , but handling online data remains a difficult hurdle to overcome . In this paper , we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking . We build local target-specific models interleaved with global optimization of the optimal data association over multiple video frames . We integrate local motion to the two predominant models in the literature , coordinate regression networks and heatmap regression networks , and combine it with late connectivity based on recurrent neural networks . The experimental results on two datasets , 300VW and SNaP-2DFe , show that local motion improves video-based face alignment and is complementary to late temporal information . Despite the simplicity of the proposed architectures , our best model provides competitive performance with more complex models from the literature . Exploiting visual actions present in non-scripted lecture videos can be useful for automatically segmenting and extracting the structure of these videos . Such visual cues help identify possible key frames , index points , key events and relevant meta-data useful for e-learning systems , video surrogates and video skims . We therefore , propose a multi-model action classification system for four predefined actions performed by instructor in lecture videos . This structure facilitates the identification of appropriate keyframes for storyboards , eliminating redundant or peripheral images , and improves the storyboard 's ability to communicate the essential message of videos . The tiered model is motivated and supported by the user study as well as current research on video surrogates and classical indexing theory . © 2007 Therefore , improving the quality of experience ( QoE ) for this service is important for customer satisfaction . In this letter we propose a QoE-aware scheduling solution for the HTTP progressive video downloading in OFDMA systems . Whereas most scheduling strategies consider QoS metrics such as delay , jitter or throughput , our proposal takes into account a metric that directly affects the end-users experience . This application note will make use of the Spartan-3A DSP development board to explain how to co-simulate Xilinx ’s highest level form of an Edgefilter on a still image and implementation sobel edge detection on real-time video based on FPGA board . This document begins by explaining how to convert a still image into a format useable by Matlab and Simulink , shows the construction of a basic algorithm using Xilinx ’s System Generator blocks , and also illustrates the procedure of implementing the design real-time video processing using the FPGA development board . The light condition and color contrast will affect the edge detection result . In GameStory : The 2019 Video Game Analytics Challenge , two main tasks are nominated to solve in the challenge , which are replay detection - multi-stream synchronization , and game story summarization . In this paper , we propose a data-driven based approach to solve the first task : replay detection - multi-stream synchronization . To facilitate further research in video-text retrieval , we release a new dataset of 50 K video-sentence pairs collected from social media , dubbed MRW ( my reaction when ) . We demonstrate our approach on both image-text and video-text retrieval scenarios using MS-COCO , TGIF , and our new MRW dataset . This paper presents a basic configuration of a system that provides dynamic delivery of full-motion video while following target users in ubiquitous computing environments . The proposed system is composed of multiple computer displays with radio frequency identification ( RFID ) tag readers , which are automatically connected to a network via IP , and RFID tags worn by users and some network servers . Many of the traditional methods of operational signal monitoring and precise signal measurement used for composite analogue video are also applicable to the newer signal formats ; however , with new techniques come different types or problems . Some of these new challenges are discussed . Digital video belongs to the field of information industry which the Country develops especially . With the rapid progress of broadband network and digital TV , the applications of video-on-demand , interactive TV and video website become more and more popular . All of these applications are facing the emerging digitalized video data in a large amount . Among the advanced coding tools of HEVC , tiles and wavefront parallel processing ( WPP ) have been newly adopted for parallel processing of such high-resolution ( 4 K/8 K-UHD ) video . To realize UHD video services over portable devices with limited battery power , it is essential to implement multi-core-based and dedicated HEVC hardware decoders that support the tile- and wavefront-based parallel processing . By doing so , each frame is divided into a multiple number of picture partitions which can then be processed by multiple hardware decoder cores in parallel .
A new approach to block-based motion estimation for video compression , called the genetic motion search ( GMS ) algorithm , is introduced . It makes use of a natural processing concept called genetic algorithm ( GA ) .
This paper presents a new kind of reference frame , i.e. , the " background frame " , to improve the accuracy of motion compensation in video compression . When an object moves in the scene of a video sequence , a certain part of the background is overlapped at first .
PURPOSE : A motion estimator for video data compression is provided to carry out motion estimation according to the most suitable algorithm selected by a user . CONSTITUTION : A motion estimator(20 ) for video data compression includes a demultiplexer(210 ) , a motion estimating unit(200 ) , and a multiplexer(230 ) .
Form the beginning of contrasting wavelet transformation with other transformation used for video compression coding , this paper expounds the advantages of wavelet transformation and introduces it 's theories and characteristics . Then the paper proposes two kinds of technologies for wavelet transforming coding , one is a coding technology based on zerotree , the other is a coding technology based on information source partition and bit plane .
The invention discloses a video inter-frame compression coding method based on macroblock features and statistical properties and relates to the field of video compression coding . The method comprisesthe following steps : firstly extracting brightness information of the current coding macroblock from original video data , adopting the dual-layer prejudgment criteria for determining the optimal inter-frame coding mode according to the statistical properties of the optimal inter-frame mode selected by the current coding macroblock , further utilizing the time-space relevant features of the currentcoding macroblock for selectively carrying out intra-frame coding , comparing the optimal inter-frame coding mode with the optimal intra-frame coding mode according to a rate distortion cost function , determining the final inter-frame coding mode and coding the current macroblock .
Distinctive features and content include using the Internet to illustrate how network protocols are designed ; state-of-the-art material on implementation and performance ( TCP congestion control , ATM networks , real-time Internet services and RSVP , routing protocols from RIP to BGP-4 and classless routing ( CIDR ) , Next Generation Internet Protocol ( IPv6 ) , network security , and video and video compression ( MPEG ) . The book employs code fragments from a working protocol environment , the x-kernel , to demonstrate how protocol specifications and design principles are implemented .
The invention provides a decoding method based on edge intensity factors for conducting the de-blocking filter in the video compression processing , wherein , the acquisition of edge intensity factors is finished before the acquisition of the predication macro-block data and the residual block data . The method has the advantages that the current macro-block information can be released from a memory area in advance , and the needed information for the reference to macro blocks share the information existed in other storage area , so that the on-chip memory space in the decoding device is not exclusively occupied , and the memory space is in no need of being externally connected .
At last , three search points is used to replace the small diamond search which in this paper is just a jump object for the most satisfied early-termination . Simulations shows that improved algorithm has a better performance in the terms of reducing motion estimation time , improving the speed of video coding compression and guaranteeing the quality of reconstructed image , comparing with UMHexagonS algorithm .
Firstly , on the basis of analysis forvideo compression standard H.264/AVC , DSP TMS320C6416 and ARMmicrocontrollerare used to implement a flexible compressed platform with high quantity embedded digital video . Then the principle of system structure , hardware designand encodingalgorithm in are illustrateddetails .
Finally , it is satisfying to note that the fitting function effectively represents the patch-wise geometry information . Moreover , new depth maps are obtained with extremely small and stable values , which are more suitable for video-based compression . Different patch-wise fitting parameters are preserved and coded using lossless compression through the open source PAQ project .
The Discrete Cosine Transform ( DCT ) is one of the basic varieties of transform coding algorithms . Moreover DCT is used in standard algorithms of compression of still image ( JPEG ) and video compression of algorithms ( MPEG , H.26x ) . In case of discrete cosine transform of compression 's images of algorithms there are used the blocs : 8x8 pixels .
From the Publisher : The Complete Guide to Video Compression Video compression is the magic behind digital TV , videoconferencing , DVDs , and Internet video . In Video Compression Demystified , specialist Peter Symes paints the full video compression picture in a friendly , not-too-big , not-too-technical introduction that explains what happens in compression , why compression works , and what will stop it from working .
With the proposed video motion representation , two new motion features for video motion content indexing are proposed : 1 ) motion histogram and 2 ) motion correlogram . We demonstrate the usefulness of the motion features with three applications : ( i ) video retrieval , ( ii ) video clustering and ( iii ) video classification . With the PCRM based video motion representation , a new motion based key frame extraction method is developed .
However , it is very difficult to obtain satisfactory classification accuracy due to traditional methods can not fully exploit multimodal information . Herein , we propose a novel approach to modeling cognitive events from EEG data by reducing it to a video classification problem , which is designed to preserve the multimodal information of EEG . In addition , optical flow is introduced to represent the variant information of EEG .
However , the train-test discrepancy of action localization strategy prevents WTAL models from leveraging semi-supervision for further improvement . At training time , attention or multiple instance learning is used to aggregate predictions of each snippet for video-level classification ; at test time , they first obtain action score sequences over time , then truncate segments of scores higher than a fixed threshold , and post-process action segments . The inconsistent strategy makes it hard to explicitly supervise the action localization model with temporal boundary annotations at training time .
Specifically , a frame-level preprocessing strategy is first proposed to efficiently decode the videos for useful information . Then an image-based model is developed to achieve the final video-level classification . For the multi-label classification case , three modules are proposed to enhance the model ’s classification capability , relieve the label-imbalance issue and exploit the label correlation respectively .
We define Unequal Secure Encryption ( USE ) as an approach that applies different encryption schemes ( with different security strength ) to different parts of compressed video data . This USE scheme includes two parts : video data classification and unequal secure video data encryption . Firstly , we classify the video data into two partitions : Important data partition and unimportant data partition .
In the past few years , the demand for real-time hardware implementations of deep neural networks ( DNNs ) , especially convolutional neural networks ( CNNs ) , has dramatically increased , thanks to their excellent performance on a wide range of recognition and classification tasks . When considering real-time action recognition and video/image classification systems , latency is of paramount importance . Therefore , applications strive to maximize the accuracy while keeping the latency under a given application-specific maximum : in most cases , this threshold can not exceed a few hundred milliseconds .
MARVIN allows for the creation of Docker containers that run on Kubernetes clusters within DARPA to provide an execution environment for automated machine learning . MARVIN currently contains over 400 datasets and challenge problems from a wide array of ML domains including routine classification and regression to advanced video/image classification and remote sensing .
The normal and disordered people balance ability classification is a key premise for rehabilitation training . This paper proposes a multi-barycentric area model ( MBAM ) , which can be applied for accurate video analysis based classification . First , we have invited fifty-three subjects to wear an HTC ( High Tech Computer Corporation ) VIVE ( Very Immersive Virtual Experience ) helmet and to walk ten meters while seeing a virtual environment .
We present a real-time multisensor architecture for combined laser scanner and infra-red video-based pedestrian detection and tracking used within a road side unit for intersection assistance . In order to achieve outmost classification performance we propose a cascaded classifier using laser scanner hypothesis generation and histogram of oriented gradients ( HOG ) descriptors for video-based classification together with linear and Gaussian kernel support vector machines ( SVM ) . The entire classification cascade is implemented on a graphics processing unit ( GPU ) .
Because of its multimodal characteristics , we decide to call this method Deep Multimodal Learning ( DML ) . Our DML-based model was trained on Google Cloud and our own server and was tested in a well-known video classification competition on Kaggle held by Google .
The method comprises : conducting segmentation on a video in a sample video library in accordance with a time sequence , so as to obtain a segmentation result and generate a motion atom set ; by using the motion atom set and the segmentation result , generating a motion phrase set which can express a complex motion mode , and generating a description vector of the video in the sample video library based on the motion phrase set ; and by using the description vector , determining a video to be detected which is of the same type as the video in the sample video library . The method is suitable for a video classification scenario .
The TOMB/P filter [ Williams , 2011 , 2015 ] is an attractive method for multiobject tracking . However , its original formulation is computationally feasible only for linear-Gaussian system models , and it suffers from the track coalescence effect .
The results of this heuristic search are combined with the more common views of target objects , and intensity information in order to update the templates . As a result , the updating process will be computed only when the target object has evolved to a transformed shape dissimilar with respect to the current shape , providing robust tracking , and multiobject tracking because accurate template updating is performed . The paper includes experimental results with inside and outside video streams demonstrating the effectiveness and efficiency for real-time machine vision based tasks in unrestricted environments .
Abstract In this paper , we address the problem of online multi-object tracking based on the Maximum a Posteriori ( MAP ) framework . Given the observations up to the current frame , we estimate the optimal object trajectories via two MAP estimation stages : object detection and data association .
We first define the tracklet confidence using the detectability and continuity of a tracklet , and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence . We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values . Based on this strategy , tracklets sequentially grow with online-provided detections , and fragmented tracklets are linked up with others without any iterative and expensive association steps .
Tracking-by-detection is a common approach for online multi-object tracking problem . At present , the following challenges still exist in the multi-object tracking scenarios : ( 1 ) The result of object re-tracking after full occlusion is not ideal ; ( 2 ) The predicted position of object is not accurate enough in the complicated video scenarios .
A kernel-based clustering algorithm is proposed to group the projected points according to their height values and locations on the plane . By producing clusters , the number , position , and orientation of objects in the surveillance scene can be determined for online multi-object detection and tracking . Experiments on both indoor and outdoor applications with complex scenes show the advantages of the proposed system .
There is a big challenge in online multi-object tracking-by-detection , which caused by frequent occlusions , false alarms or miss detections and other factors . In this paper , we proposed an improved fast online multi-object tracking method through taking into account the results of multiple single-object trackers and detections synthetically .
The paper presents a novel method for online tracking of multiple objects with non-overlapping cameras . The method is based on a generative model defining probabilistic dependencies between observations , the underlying color properties of objects and their dynamics .
Abstract In this paper , a novel fuzzy logic data association algorithm is proposed for online visual multi-object tracking . Firstly , in the proposed algorithm , in order to incorporate expert experience into the data association for the improvement of performance in multi-object tracking , a fuzzy inference system based on knowledge is designed by using a set of fuzzy if-then rules .
Global optimization algorithms have shown impressive performance in data-association-based multi-object tracking , but handling online data remains a difficult hurdle to overcome . In this paper , we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking . We build local target-specific models interleaved with global optimization of the optimal data association over multiple video frames .
We integrate local motion to the two predominant models in the literature , coordinate regression networks and heatmap regression networks , and combine it with late connectivity based on recurrent neural networks . The experimental results on two datasets , 300VW and SNaP-2DFe , show that local motion improves video-based face alignment and is complementary to late temporal information . Despite the simplicity of the proposed architectures , our best model provides competitive performance with more complex models from the literature .
Exploiting visual actions present in non-scripted lecture videos can be useful for automatically segmenting and extracting the structure of these videos . Such visual cues help identify possible key frames , index points , key events and relevant meta-data useful for e-learning systems , video surrogates and video skims . We therefore , propose a multi-model action classification system for four predefined actions performed by instructor in lecture videos .
This structure facilitates the identification of appropriate keyframes for storyboards , eliminating redundant or peripheral images , and improves the storyboard 's ability to communicate the essential message of videos . The tiered model is motivated and supported by the user study as well as current research on video surrogates and classical indexing theory . © 2007
Therefore , improving the quality of experience ( QoE ) for this service is important for customer satisfaction . In this letter we propose a QoE-aware scheduling solution for the HTTP progressive video downloading in OFDMA systems . Whereas most scheduling strategies consider QoS metrics such as delay , jitter or throughput , our proposal takes into account a metric that directly affects the end-users experience .
This application note will make use of the Spartan-3A DSP development board to explain how to co-simulate Xilinx ’s highest level form of an Edgefilter on a still image and implementation sobel edge detection on real-time video based on FPGA board . This document begins by explaining how to convert a still image into a format useable by Matlab and Simulink , shows the construction of a basic algorithm using Xilinx ’s System Generator blocks , and also illustrates the procedure of implementing the design real-time video processing using the FPGA development board . The light condition and color contrast will affect the edge detection result .
In GameStory : The 2019 Video Game Analytics Challenge , two main tasks are nominated to solve in the challenge , which are replay detection - multi-stream synchronization , and game story summarization . In this paper , we propose a data-driven based approach to solve the first task : replay detection - multi-stream synchronization .
To facilitate further research in video-text retrieval , we release a new dataset of 50 K video-sentence pairs collected from social media , dubbed MRW ( my reaction when ) . We demonstrate our approach on both image-text and video-text retrieval scenarios using MS-COCO , TGIF , and our new MRW dataset .
This paper presents a basic configuration of a system that provides dynamic delivery of full-motion video while following target users in ubiquitous computing environments . The proposed system is composed of multiple computer displays with radio frequency identification ( RFID ) tag readers , which are automatically connected to a network via IP , and RFID tags worn by users and some network servers .
Many of the traditional methods of operational signal monitoring and precise signal measurement used for composite analogue video are also applicable to the newer signal formats ; however , with new techniques come different types or problems . Some of these new challenges are discussed .
Digital video belongs to the field of information industry which the Country develops especially . With the rapid progress of broadband network and digital TV , the applications of video-on-demand , interactive TV and video website become more and more popular . All of these applications are facing the emerging digitalized video data in a large amount .
Among the advanced coding tools of HEVC , tiles and wavefront parallel processing ( WPP ) have been newly adopted for parallel processing of such high-resolution ( 4 K/8 K-UHD ) video . To realize UHD video services over portable devices with limited battery power , it is essential to implement multi-core-based and dedicated HEVC hardware decoders that support the tile- and wavefront-based parallel processing . By doing so , each frame is divided into a multiple number of picture partitions which can then be processed by multiple hardware decoder cores in parallel .
Genetic motion search algorithm for video compression Background-frame based motion compensation for video compression Apparatus for estimating motion for compressing image data Analysis and research of video compression coding technology based on wavelet transformation Video inter-frame compression coding method based on macroblock features and statistical properties CDMA for wireless personal communications [Books] Decode method and apparatus for de-block effect filtering based on boundary strength factor Improved Umhexagons Algorithm Used in H.264 Embedded networked video acquisition and compressed system based on RTP Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting Chen and Loeffler Fast DCT modified Algorithms Implemented in FPGA Chips for Real-Time Image Compression Video Compression Demystified Motion based video content indexing. Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography Towards Train-Test Consistency for Semi-supervised Temporal Action Localization. A Real-Time Multi-Label Classification System for Short Videos An Unequal Secure Encryption Scheme for H.264/AVC Video Compression Standard An Architecture to Accelerate Convolution in Deep Neural Networks MARVIN: An Open Machine Learning Corpus and Environment for Automated Machine Learning Primitive Annotation and Execution Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment Gpu architecture for stationary multisensor pedestrian detection at smart intersections Deep Multimodal Learning: An Effective Method for Video Classification Video classification method and device Sequential Monte Carlo implementation of the track-oriented marginal multi-Bernoulli/poisson filter Tracking Deformable Objects with Evolving Templates for Real-Time Machine Vision Online maximum a posteriori tracking of multiple objects using sequential trajectory prior Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking Online multi-object tracking with pedestrian re-identification and occlusion processing Multi-object detection and tracking by stereo vision Robust online multi-object tracking based on KCF trackers and reassignment Online multicamera tracking with a switching state-space model Fuzzy logic approach to visual multi-object tracking A Hybrid Data Association Framework for Robust Online Multi-Object Tracking Video-Based Face Alignment With Local Motion Modeling Exploiting Visual Cues in Non-Scripted Lecture Videos for Multi-modal Action Recognition Prioritization strategies for video storyboard keyframes A QoE-Aware Scheduler for HTTP Progressive Video in OFDMA Systems Hardware Software Co-Simulation and Real-Time Video Processing For Edge Detection Using Matlab Simulink Model Blockset Replay Detection and Multi-stream Synchronization in CS: GO Game Streams Using Content-Based Image Retrieval and Image Signature Matching Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval Reliable Partner System Always Providing Users with Companionship Through Video Streaming Measurement and monitoring (of video signals) Audio Filtering Technology Application Analysis and Discussion in Video Summarization System Efficient In-Loop Filtering Across Tile Boundaries for Multi-Core HEVC Hardware Decoders With 4 K/8 K-UHD Video Applications
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ICME ICASSP ICONIP (2) ICIP Intelligent Vehicles Symposium FUSION GlobalSIP ICPR (4) WACV SITIS CVPR HCI (3)
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