It can be quickly and effectively deployed in networks . A prototype implementation of the proposed mechanism is completed , which is tested under a video conference application over the CERNET2 topology . The results indicate that it is feasible and effective with good performance . The optimization of motion estimation in H.264/AVC encoder for video conference application is proposed . Firstly , an advanced diamond search algorithm is proposed . Video quality measurement takes an important role in many applications . Full-reference quality metrics which are usually used in video codecs comparisons are expected to reflect any changes in videos . Based on these observations , a study of distortion degree and quality variation that occurs in video from packet losses was launched . Subsequently it is proposed to introduce a new framework for video quality measurement , which takes into account quality variations arising from channel errors . We label a sequence as suspicious if it maps to an existing model of suspicious behavior or does not map to any existing model according to the corresponding HMMs . We evaluate our proposed method on a real-world video surveillance and find that the method is very effective at detecting suspicious behavior . On the other , t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain , which extends the traditional matrix nuclear norm in a block circulant way . We test the t-TNN model on a video completion application that aims to fill missing values and the experiment results validate its effectiveness , especially when dealing with video recorded by a non-stationary panning camera . The block circulant matricization of the twist tensor can be transformed into a circulant block representation with nuclear norm invariance . On the other , t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain , which extends the traditional matrix nuclear norm in a block circulant way . We test the t-TNN model on a video completion application that aims to fill missing values and the experiment results validate its effectiveness , especially when dealing with video recorded by a nonstationary panning camera . The block circulant matricization of the twist tensor can be transformed into a circulant block representation with nuclear norm invariance . Automatic video description generation has recently been getting attention after rapid advancement in image caption generation . Automatically generating description for a video is more challenging than for an image due to its temporal dynamics of frames . Automatic video description generation ( a.k.a video captioning ) is one of the ultimate goals for video understanding . Despite the wide range of applications such as video indexing and retrieval etc . , the video captioning task remains quite challenging due to the complexity and diversity of video content . In this work , we introduce the basic concepts for the video transmission in HCMN systems . We also detail main existing mechanisms , which aim to improve the performance of HCMN system in sharing video content , including Quality of Experience (QoE)-based solutions for handover , routing , error correction , decisionmaking , and controlling the dissemination of video flows in wireless multimedia-aware Decomposing video frames into coherent 2D motion layers is a powerful method for representing videos . Such a representation provides an intermediate description that enables applications such as object tracking , video summarization and visualization , video insertion , and sprite-based video compression . Previous work on motion layer analysis has largely concentrated on two-frame or multi-frame batch formulations . Although promising results have been achieved in video captioning , existing models are limited to the fixed inventory of activities in the training corpus , and do not generalize to open vocabulary scenarios . Here we introduce a novel task , zero-shot video captioning , that aims at describing out-of-domain videos of unseen activities . Videos of different activities usually require different captioning strategies in many aspects , i.e. word selection , semantic construction , and style expression etc , which poses a great challenge to depict novel activities without paired training data . The second algorithm makes use of wide spaces of one color that happens in videos with text and graphics , which are frequently found in video conference applications . The results show that our algorithms accelerate encoding of video conference sequences by an average of 22.19 % with a negligible effect on the compression efficiency . This paper describes a set of software tools we developed for scalable live video coding and transmission in Max/MSP/Jitter . The tools are available to the users as the mcl.jit object library , which includes several real-time scalable codecs , some with support for Region-Of-Interest ( ROI ) coding , as well as tools for scalable live video transmission . Some of these tools have been successfully used in live telematic performances . This paper describes multi-component spatiotemporal attention mechanisms in application to object detection in videos . The detection of objects of interest relies on the analysis of feature-point areas ( FPAs ) , which correspond to the object-relevant focus-of-attention ( FoA ) points extracted by the proposed spatiotemporal mechanisms of attention focusing . In this context , the thesis makes two major contributions . First , we propose several formulations and extensions of deep learning methods which learn hierarchical representations for three challenging video analysis tasks , including complex event recognition , object detection in videos and measuring action similarity . The proposed methods are extensively demonstrated for each work on the state-of-the-art challenging datasets . To solve this challenge , we present a method to jointly discover and segment an object from a noisy video , where the target disappears intermittently throughout the video . Previous methods either only fulfill video object discovery , or video object segmentation presuming the existence of the object in each frame . We argue that jointly conducting the two tasks in a unified way will be beneficial . In experiments , we show that the improvement in the selection power , the training of multiple students and the increase in unlabeled data significantly improve segmentation accuracy from one generation to the next . Our method achieves top results on three current datasets for object discovery in video , unsupervised image segmentation and saliency detection . At test time , the proposed system is fast , being one to two orders of magnitude faster than published unsupervised methods . A Unified Framework for Visual Object Discovery Through Dialogs and Queries . Recognising objects according to a pre-defined fixed set of class labels has been well studied in the Computer Vision . Visual Object Discovery through Multi-modal Dialogue . We introduce GuessWhat ? ! , a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems . A crucial advantage of our method is that it learns a new deep similarity metric , to distinguish multiple objects in one image . We demonstrate that the network can act as an encoder-decoder generating parts of an image which contain an object , or as a modified deep CNN to represent images for object detection and discovery . Our ranking GAN offers a novel way to search through images for visual patterns . It is called T-CNN , i.e. , tubelets with convolutional neueral networks . The proposed framework won newly introduced an object-detection-from-video task with provided data in the ImageNet Large-Scale Visual Recognition Challenge 2015 . Code is publicly available at https://github.com/myfavouritekk/T-CNN . Extensive experiments validated the efficiency of our proposed approach on several data sets . It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization . In addition , compared with discriminative methods , the naturally existing object classes in the given image collection can be subtly discovered , which makes our approach well suited for realistic applications of unsupervised object discovery . Despite recent advances in joint processing of images , sometimes it may not be as effective as single image processing for object discovery problems . In this paper while aiming for common object detection , we attempt to address this problem by proposing a novel QCCE : Through a robust topic consistency potential ( RTCP ) modelling approach , we perform accurate multi-class segmentation on high-resolution remote-sensing images . The generated segments can be readily used for object recognition and discovery . We report satisfactory the performance on two sets of high-resolution remote-sensing images that cover a highly populated urban area and a rural area , respectively . In this paper , we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing . In video compressive sensing one frame is acquired using a set of coded masks ( sensing matrix ) from which a number of video frames is reconstructed , equal to the number of coded masks . There exist several applications in image processing ( eg : video compressed sensing and color image demosaicing ) which require separation of constituent images given measurements in the form of a coded superposition of those images . Physically practical code patterns in these applications are non-negative and do not obey the nice coherence properties of other patterns such as Gaussian codes , which can adversely affect reconstruction performance . Compressive video sensing is the process of encoding multiple sub-frames into a single frame with controlled sensor exposures and reconstructing the sub-frames from the single compressed frame . It is known that spatially and temporally random exposures provide the most balanced compression in terms of signal recovery . Several recent studies in compressive video sensing have realized scene capture beyond the fundamental trade-off limit between spatial resolution and temporal resolution using random space-time sampling . However , most of these studies showed results for higher frame rate video that were produced by simulation experiments or using an optically simulated random sampling camera , because there are currently no commercially available image sensors with random exposure or sampling capabilities . Aiming at enhance reconstruction quality , this paper proposes an adaptive reconstruction scheme with no feedback channel for distributed compressive video sensing , effectively exploiting temporal correlation . Specifically , the proposed scheme divides each block of non-key frames into different classifications based on temporal correlation in the encoding side and selects corresponding reconstruction mode which adaptively utilizes side information in the decoding side . Distributed Compressive Video Sensing : Jointly Exploiting the HEVC Motion Estimation and the ℓ1 – ℓ1 Reconstruction . Almost all existing multihypothesis ( MH ) prediction methods in compressive video sensing ( CVS ) are absorbed in exploiting the reference information in key frames to guide the reconstruction of non-key frames . However , when the non-key frames are distant from key frames , the temporal correlation between them declines . These approaches update the network weights keeping latent codes fixed to reconstruct the target image from the given measurements . In this paper , we optimize over network weights and latent codes to use untrained generative network as prior for video compressive sensing problem . We show that by optimizing over latent code , we can additionally get concise representation of the frames which retain the structural similarity of the video frames . We need a scalable theory of sensing ; one such theory is compressive sensing . In this paper , we will present an extensive overview of computational imaging and compressive sensing techniques as applied to the video compressive sensing problem . We show the interplay of three core ideas : signal models , imaging architectures and recovery algorithms . Snapshot compressive imaging ( SCI ) refers to compressive imaging systems where multiple frames are mapped into a single measurement , with video compressive imaging and hyperspectral compressive imaging as two representative applications . Though exciting results of high-speed videos and hyperspectral images have been demonstrated , the poor reconstruction quality precludes SCI from wide applications . Deep neural networks have been applied to video compressive sensing ( VCS ) task recently . The existing DNN-based VCS methods compress and reconstruct the scene video only in space or time dimensions , which ignores the spatial-temporal correlation of the video . During the two-year operation of the test-site , the soils have evolved to reflect real field soil conditions . This paper compares visual observations as well as ground-penetrating radar and thermal infrared measurements at this site taken immediately after construction in early 2004 with measurements from early 2006 . The visual observations reveal that the 2006 soil surfaces exhibit a much higher spatial variability due to the development of mini-reliefs , " loose " and " connected " soil crusts , cracks in clay soils , and vegetation . The Landsat TM sensor for thermal infrared measurements , Band 6 , has lower resolution than those of other six bands . The authors propose a statistical approach to enhance the resolution of low resolution thermal infrared image by using other band images . The thermal infrared camera with the uncooled microbolometer array based on commercial products has been developed in a laboratory of a Japanese university and mounted to a 50-kg class small satellite specialized for discovering wildfire . It has been launched in 2014 and successfully detected considerable hotspots not only wildfire but also volcanoes . However , there are few studies in this field mainly because thermal infrared images have several unwanted attributes , which make it difficult to obtain the discriminative features of the target . Considering the powerful representational ability of convolutional neural networks and their successful application in visual tracking , we transfer the pre-trained convolutional neural networks based on visible images to thermal infrared tracking . We observe that the features from the fully-connected layer are not suitable for thermal infrared tracking due to the lack of spatial information of the target , while the features from the convolution layers are . Besides , the features from a single convolution layer are not robust to various challenges . Furthermore , a simple but effective scale estimation strategy is exploited to boost the tracking accuracy . To evaluate the performance of the proposed tracker , we carry out experiments on two thermal infrared tracking benchmarks : VOT-TIR 2015 and VOT-TIR 2016 . The experimental results demonstrate that our tracker is effective and achieves promising performance . Abstract Thermal infrared ( TIR ) object tracking is one of the most challenging tasks in computer vision . This paper proposes a robust TIR tracker based on the continuous correlation filters and adaptive feature fusion ( RCCF-TIR ) . The compact thermal imager ( CTI ) is a dual-band , strained-layer-superlattice ( SLS ) detector-based instrument that was installed on the exterior of the International Space Station ( ISS ) in conjunction with the third Robotic Refueling Mission 3 ( RRM3 ) in 2018 . The CTI serves as a pathfinder for future thermal infrared capability on Landsat . The CTI incorporates an SLS hybrid , a dual-band 3 - 5 and 8 - 10 μm , electrically switchable , 320 × 256 array with 30 μm2 pixels , bonded to an Indigo ISC0903 Readout Integrated Circuit ( ROIC ) . The proposed WLSD is naturally invariant to the shape scale and rotation , and has low computation load as a whole shape descriptor . We further apply WLSD in thermal infrared object tracking , and propose a multi-feature integration method based on WLSD shape , target area and target trajectory . The experiments are first conducted on MPEG-7 and Tari shape dataset to test the shape discriminative capacity of WLSD , and then tested on the infrared tracking videos to validate the proposed multi-feature integration tracking framework . Abstract Based on the technique of background subtraction , two level set based active contour models ( LSACs ) named as RT-BSLSAC and EA-BSLSAC are proposed for human segmentation in thermal infrared surveillance systems . The energy functional of RT-BSLSAC is initially formulated with the spatial – temporal information extracted from the background-subtracted images that correspond to the current frame and its adjacent frames . Thermal infrared ( TIR ) pedestrian tracking is one of the important components among numerous applications of computer vision , which has a major advantage : it can track pedestrians in total darkness . The ability to evaluate the TIR pedestrian tracker fairly , on a benchmark dataset , is significant for the development of this field . Thermal infrared ( TIR ) target tracking is a challenging task as it entails learning an effective model to identify the target in the situation of poor target visibility and clutter background . The sparse representation , as a typical appearance modeling approach , has been successfully exploited in the TIR target tracking .
It can be quickly and effectively deployed in networks . A prototype implementation of the proposed mechanism is completed , which is tested under a video conference application over the CERNET2 topology . The results indicate that it is feasible and effective with good performance .
The optimization of motion estimation in H.264/AVC encoder for video conference application is proposed . Firstly , an advanced diamond search algorithm is proposed .
Video quality measurement takes an important role in many applications . Full-reference quality metrics which are usually used in video codecs comparisons are expected to reflect any changes in videos .
Based on these observations , a study of distortion degree and quality variation that occurs in video from packet losses was launched . Subsequently it is proposed to introduce a new framework for video quality measurement , which takes into account quality variations arising from channel errors .
We label a sequence as suspicious if it maps to an existing model of suspicious behavior or does not map to any existing model according to the corresponding HMMs . We evaluate our proposed method on a real-world video surveillance and find that the method is very effective at detecting suspicious behavior .
On the other , t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain , which extends the traditional matrix nuclear norm in a block circulant way . We test the t-TNN model on a video completion application that aims to fill missing values and the experiment results validate its effectiveness , especially when dealing with video recorded by a non-stationary panning camera . The block circulant matricization of the twist tensor can be transformed into a circulant block representation with nuclear norm invariance .
On the other , t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain , which extends the traditional matrix nuclear norm in a block circulant way . We test the t-TNN model on a video completion application that aims to fill missing values and the experiment results validate its effectiveness , especially when dealing with video recorded by a nonstationary panning camera . The block circulant matricization of the twist tensor can be transformed into a circulant block representation with nuclear norm invariance .
Automatic video description generation has recently been getting attention after rapid advancement in image caption generation . Automatically generating description for a video is more challenging than for an image due to its temporal dynamics of frames .
Automatic video description generation ( a.k.a video captioning ) is one of the ultimate goals for video understanding . Despite the wide range of applications such as video indexing and retrieval etc . , the video captioning task remains quite challenging due to the complexity and diversity of video content .
In this work , we introduce the basic concepts for the video transmission in HCMN systems . We also detail main existing mechanisms , which aim to improve the performance of HCMN system in sharing video content , including Quality of Experience (QoE)-based solutions for handover , routing , error correction , decisionmaking , and controlling the dissemination of video flows in wireless multimedia-aware
Decomposing video frames into coherent 2D motion layers is a powerful method for representing videos . Such a representation provides an intermediate description that enables applications such as object tracking , video summarization and visualization , video insertion , and sprite-based video compression . Previous work on motion layer analysis has largely concentrated on two-frame or multi-frame batch formulations .
Although promising results have been achieved in video captioning , existing models are limited to the fixed inventory of activities in the training corpus , and do not generalize to open vocabulary scenarios . Here we introduce a novel task , zero-shot video captioning , that aims at describing out-of-domain videos of unseen activities . Videos of different activities usually require different captioning strategies in many aspects , i.e. word selection , semantic construction , and style expression etc , which poses a great challenge to depict novel activities without paired training data .
The second algorithm makes use of wide spaces of one color that happens in videos with text and graphics , which are frequently found in video conference applications . The results show that our algorithms accelerate encoding of video conference sequences by an average of 22.19 % with a negligible effect on the compression efficiency .
This paper describes a set of software tools we developed for scalable live video coding and transmission in Max/MSP/Jitter . The tools are available to the users as the mcl.jit object library , which includes several real-time scalable codecs , some with support for Region-Of-Interest ( ROI ) coding , as well as tools for scalable live video transmission . Some of these tools have been successfully used in live telematic performances .
This paper describes multi-component spatiotemporal attention mechanisms in application to object detection in videos . The detection of objects of interest relies on the analysis of feature-point areas ( FPAs ) , which correspond to the object-relevant focus-of-attention ( FoA ) points extracted by the proposed spatiotemporal mechanisms of attention focusing .
In this context , the thesis makes two major contributions . First , we propose several formulations and extensions of deep learning methods which learn hierarchical representations for three challenging video analysis tasks , including complex event recognition , object detection in videos and measuring action similarity . The proposed methods are extensively demonstrated for each work on the state-of-the-art challenging datasets .
To solve this challenge , we present a method to jointly discover and segment an object from a noisy video , where the target disappears intermittently throughout the video . Previous methods either only fulfill video object discovery , or video object segmentation presuming the existence of the object in each frame . We argue that jointly conducting the two tasks in a unified way will be beneficial .
In experiments , we show that the improvement in the selection power , the training of multiple students and the increase in unlabeled data significantly improve segmentation accuracy from one generation to the next . Our method achieves top results on three current datasets for object discovery in video , unsupervised image segmentation and saliency detection . At test time , the proposed system is fast , being one to two orders of magnitude faster than published unsupervised methods .
A Unified Framework for Visual Object Discovery Through Dialogs and Queries . Recognising objects according to a pre-defined fixed set of class labels has been well studied in the Computer Vision .
Visual Object Discovery through Multi-modal Dialogue . We introduce GuessWhat ? ! , a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems .
A crucial advantage of our method is that it learns a new deep similarity metric , to distinguish multiple objects in one image . We demonstrate that the network can act as an encoder-decoder generating parts of an image which contain an object , or as a modified deep CNN to represent images for object detection and discovery . Our ranking GAN offers a novel way to search through images for visual patterns .
It is called T-CNN , i.e. , tubelets with convolutional neueral networks . The proposed framework won newly introduced an object-detection-from-video task with provided data in the ImageNet Large-Scale Visual Recognition Challenge 2015 . Code is publicly available at https://github.com/myfavouritekk/T-CNN .
Extensive experiments validated the efficiency of our proposed approach on several data sets . It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization . In addition , compared with discriminative methods , the naturally existing object classes in the given image collection can be subtly discovered , which makes our approach well suited for realistic applications of unsupervised object discovery .
Despite recent advances in joint processing of images , sometimes it may not be as effective as single image processing for object discovery problems . In this paper while aiming for common object detection , we attempt to address this problem by proposing a novel QCCE :
Through a robust topic consistency potential ( RTCP ) modelling approach , we perform accurate multi-class segmentation on high-resolution remote-sensing images . The generated segments can be readily used for object recognition and discovery . We report satisfactory the performance on two sets of high-resolution remote-sensing images that cover a highly populated urban area and a rural area , respectively .
In this paper , we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing . In video compressive sensing one frame is acquired using a set of coded masks ( sensing matrix ) from which a number of video frames is reconstructed , equal to the number of coded masks .
There exist several applications in image processing ( eg : video compressed sensing and color image demosaicing ) which require separation of constituent images given measurements in the form of a coded superposition of those images . Physically practical code patterns in these applications are non-negative and do not obey the nice coherence properties of other patterns such as Gaussian codes , which can adversely affect reconstruction performance .
Compressive video sensing is the process of encoding multiple sub-frames into a single frame with controlled sensor exposures and reconstructing the sub-frames from the single compressed frame . It is known that spatially and temporally random exposures provide the most balanced compression in terms of signal recovery .
Several recent studies in compressive video sensing have realized scene capture beyond the fundamental trade-off limit between spatial resolution and temporal resolution using random space-time sampling . However , most of these studies showed results for higher frame rate video that were produced by simulation experiments or using an optically simulated random sampling camera , because there are currently no commercially available image sensors with random exposure or sampling capabilities .
Aiming at enhance reconstruction quality , this paper proposes an adaptive reconstruction scheme with no feedback channel for distributed compressive video sensing , effectively exploiting temporal correlation . Specifically , the proposed scheme divides each block of non-key frames into different classifications based on temporal correlation in the encoding side and selects corresponding reconstruction mode which adaptively utilizes side information in the decoding side .
Distributed Compressive Video Sensing : Jointly Exploiting the HEVC Motion Estimation and the ℓ1 – ℓ1 Reconstruction .
Almost all existing multihypothesis ( MH ) prediction methods in compressive video sensing ( CVS ) are absorbed in exploiting the reference information in key frames to guide the reconstruction of non-key frames . However , when the non-key frames are distant from key frames , the temporal correlation between them declines .
These approaches update the network weights keeping latent codes fixed to reconstruct the target image from the given measurements . In this paper , we optimize over network weights and latent codes to use untrained generative network as prior for video compressive sensing problem . We show that by optimizing over latent code , we can additionally get concise representation of the frames which retain the structural similarity of the video frames .
We need a scalable theory of sensing ; one such theory is compressive sensing . In this paper , we will present an extensive overview of computational imaging and compressive sensing techniques as applied to the video compressive sensing problem . We show the interplay of three core ideas : signal models , imaging architectures and recovery algorithms .
Snapshot compressive imaging ( SCI ) refers to compressive imaging systems where multiple frames are mapped into a single measurement , with video compressive imaging and hyperspectral compressive imaging as two representative applications . Though exciting results of high-speed videos and hyperspectral images have been demonstrated , the poor reconstruction quality precludes SCI from wide applications .
Deep neural networks have been applied to video compressive sensing ( VCS ) task recently . The existing DNN-based VCS methods compress and reconstruct the scene video only in space or time dimensions , which ignores the spatial-temporal correlation of the video .
During the two-year operation of the test-site , the soils have evolved to reflect real field soil conditions . This paper compares visual observations as well as ground-penetrating radar and thermal infrared measurements at this site taken immediately after construction in early 2004 with measurements from early 2006 . The visual observations reveal that the 2006 soil surfaces exhibit a much higher spatial variability due to the development of mini-reliefs , " loose " and " connected " soil crusts , cracks in clay soils , and vegetation .
The Landsat TM sensor for thermal infrared measurements , Band 6 , has lower resolution than those of other six bands . The authors propose a statistical approach to enhance the resolution of low resolution thermal infrared image by using other band images .
The thermal infrared camera with the uncooled microbolometer array based on commercial products has been developed in a laboratory of a Japanese university and mounted to a 50-kg class small satellite specialized for discovering wildfire . It has been launched in 2014 and successfully detected considerable hotspots not only wildfire but also volcanoes .
However , there are few studies in this field mainly because thermal infrared images have several unwanted attributes , which make it difficult to obtain the discriminative features of the target . Considering the powerful representational ability of convolutional neural networks and their successful application in visual tracking , we transfer the pre-trained convolutional neural networks based on visible images to thermal infrared tracking . We observe that the features from the fully-connected layer are not suitable for thermal infrared tracking due to the lack of spatial information of the target , while the features from the convolution layers are . Besides , the features from a single convolution layer are not robust to various challenges .
Furthermore , a simple but effective scale estimation strategy is exploited to boost the tracking accuracy . To evaluate the performance of the proposed tracker , we carry out experiments on two thermal infrared tracking benchmarks : VOT-TIR 2015 and VOT-TIR 2016 . The experimental results demonstrate that our tracker is effective and achieves promising performance .
Abstract Thermal infrared ( TIR ) object tracking is one of the most challenging tasks in computer vision . This paper proposes a robust TIR tracker based on the continuous correlation filters and adaptive feature fusion ( RCCF-TIR ) .
The compact thermal imager ( CTI ) is a dual-band , strained-layer-superlattice ( SLS ) detector-based instrument that was installed on the exterior of the International Space Station ( ISS ) in conjunction with the third Robotic Refueling Mission 3 ( RRM3 ) in 2018 . The CTI serves as a pathfinder for future thermal infrared capability on Landsat . The CTI incorporates an SLS hybrid , a dual-band 3 - 5 and 8 - 10 μm , electrically switchable , 320 × 256 array with 30 μm2 pixels , bonded to an Indigo ISC0903 Readout Integrated Circuit ( ROIC ) .
The proposed WLSD is naturally invariant to the shape scale and rotation , and has low computation load as a whole shape descriptor . We further apply WLSD in thermal infrared object tracking , and propose a multi-feature integration method based on WLSD shape , target area and target trajectory . The experiments are first conducted on MPEG-7 and Tari shape dataset to test the shape discriminative capacity of WLSD , and then tested on the infrared tracking videos to validate the proposed multi-feature integration tracking framework .
Abstract Based on the technique of background subtraction , two level set based active contour models ( LSACs ) named as RT-BSLSAC and EA-BSLSAC are proposed for human segmentation in thermal infrared surveillance systems . The energy functional of RT-BSLSAC is initially formulated with the spatial – temporal information extracted from the background-subtracted images that correspond to the current frame and its adjacent frames .
Thermal infrared ( TIR ) pedestrian tracking is one of the important components among numerous applications of computer vision , which has a major advantage : it can track pedestrians in total darkness . The ability to evaluate the TIR pedestrian tracker fairly , on a benchmark dataset , is significant for the development of this field .
Thermal infrared ( TIR ) target tracking is a challenging task as it entails learning an effective model to identify the target in the situation of poor target visibility and clutter background . The sparse representation , as a typical appearance modeling approach , has been successfully exploited in the TIR target tracking .
An IPv6 Based QoS Unicast Routing Mechanism Optimization of motion estimation in H.264/AVC encoder for video conference application Hacking VMAF with Video Color and Contrast Distortion. Оценка изменения качества видео во времени в сетях с потерями пакетов A Novel Approach Automatic Detection of Suspicious Behavior A New Low-Rank Tensor Model for Video Completion The Twist Tensor Nuclear Norm for Video Completion Generating Video Description using Sequence-to-sequence Model with Temporal Attention Generating Video Descriptions With Latent Topic Guidance Multimedia Human-Centric Networking: Concepts, Technologies and Trends Object Tracking with Bayesian Estimation of Dynamic Layer Representations Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning Toward Faster HEVC for Video Conference Applications Mcl.jit library for scalable live video in Max/Msp/Jitter Multi-Component Spatiotemporal Attention and its Application to Object Detection in Surveillance Videos Learning Hierarchical Representations For Video Analysis Using Deep Learning Joint Video Object Discovery and Segmentation by Coupled Dynamic Markov Networks Unsupervised Learning of Foreground Object Segmentation Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries GuessWhat?! Visual Object Discovery through Multi-modal Dialogue Object Discovery By Generative Adversarial & Ranking Networks T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization QCCE: Quality constrained co-saliency estimation for common object detection Accurate object retrieval for high-resolution remote-sensing imagery using high-order topic consistency potentials DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing Optimizing Codes for Source Separation in Compressed Video Recovery and Color Image Demosaicing Joint Optimization for Compressive Video Sensing and Reconstruction Under Hardware Constraints High-speed imaging using CMOS image sensor with quasi pixel-wise exposure Distributed Compressive Video Sensing with Adaptive Reconstruction Based on Temporal Correlation High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ1 – ℓ1 Reconstruction The Optimization of Extended Hypothesis Set for Compressive Video Sensing Generative Models for Low-Dimensional Video Representation and Compressive Sensing Compressive Sensing for Video Applications Rank Minimization for Snapshot Compressive Imaging Perceptual spatial-temporal video compressive sensing network New Mexico Tech landmine, UXO, IED detection sensor test facility: measurements in real field soils Spatial resolution enhancement of low resolution thermal infrared image based on other high resolution band images Detection of Small Wildfire by Thermal Infrared Camera With the Uncooled Microbolometer Array for 50-kg Class Satellite Deep convolutional neural networks for thermal infrared object tracking Robust thermal infrared object tracking with continuous correlation filters and adaptive feature fusion Strained-layer-superlattice-based compact thermal imager for the International Space Station. Combating tracking drift : developing robust object tracking methods Background subtraction based level sets for human segmentation in thermal infrared surveillance systems PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking
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COLING CCECE AVSS CVPR CVPR VCIP ECCV (10) ICCP
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