Inspired by recent work in machine translation and object detection , we propose a new approach for video captioning which aims to incorporate Knowledge Base information with frame features of the video . We compare and analyze our approach with prior work and show that the large volumes information is available to generate video description . We experiment with our ideas on the S2VT model , and we demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks . Temporal attention has been widely used in video description to adaptively focus on important frames . However , most existing methods based on temporal attention suffer from the problems of recognition error and detail missing , because only coarse frame-level global features are employed . A digital video camera system that provides a video summary , using a method comprising the following steps . A step of capturing a video sequence of a scene using an image sensor , a video sequence step comprises a time sequence of image frames ; step of specifying the reference data that includes predetermined human form a digital video file step using video processing path for processing a captured video sequence to ; during the video sequence capture , by using the human recognition algorithm to identify a subset of an image frame including a predetermined human , the step of analyzing the captured image frame ; a step of forming a video summary containing fewer image frames than all of the image frames in the captured video sequence , video summary includes a predetermined human Features The storing step into the storage memory digital video file ; has been the subset of the image frame steps comprising at least partially the step storing a representation of the video summaries in the storage memory is . Video summary is important for users to quickly understand the video content . In this paper , first , some selection principles of video content and an importance evaluation model of movie summary are analyzed . Working with long temporal intervals ( up to 100 frames ) enables our system to significantly reduce annotation effort with respect to state of the art systems . Even though the segmentation results are less than perfect , they are obtained efficiently and can be used in weakly supervised learning from video or for video content description . We do not rely on a discriminative object appearance model and allow extracting multiple foreground objects together , saving user time if more than one object is present . Specially , aiming at the solution of the deepening video application on intelligent transportation and public security , the video structured description technique on android platform was utilized to execute the scene description task and implement the recognition task such as face recognition and vehicle recognition . The schema of video structured description on mobile computing architecture was figured out and the synthesis searching framework was introduced by utilized the semantic web search engine . As our experiments proved , the OpenCV for Android based video analysis and event detection is eligible for the present ARM-based hardware configuration . CNNs are pretrained on object and/or action recognition tasks and used to encode video-level features . The decoder is then optimised on such static features to generate the video 's description . This disjoint setup is arguably sub-optimal for input ( video ) to output ( description ) mapping . CNNs are pretrained on object and/or action recognition tasks and used to encode video-level features . The decoder is then optimised on such static features to generate the video 's description . This disjoint setup is arguably sub-optimal for input ( video ) to output ( description ) mapping . Classical approaches were followed by a very short era of statistical methods that were soon replaced with deep learning , the current state-of-the-art in video description . Our survey shows that despite the fast-paced developments , video description research is still in its infancy due to the following reasons : Analysis of video description models is challenging , because it is difficult to ascertain the contributions towards accuracy or errors of the visual features and the adopted language model in the final description . Existing datasets neither contain adequate visual diversity nor complexity of linguistic structures . In this paper we propose a framework for development of video analysis and description systems , in an easy and interactive way . Due to the architecture design the developed software can run on different operating systems and on distributed environments . This step uses optical flow along with dense speed-up-robust-features ( SURF ) in order to detect and track moving humans in moving fields of view . The video description step is based on a fusion process that combines displacement and spatio-temporal descriptors . Experiments are carried out on the big data-set UCF-101 . The theory of ontology is applied to the video feature description together with its interface in MPEG-7 and video information based on semantic hierarchy , to build up a video semantic description model . This model makes use of the RDFS and Owl 's good capability to describe the semantic information and self-extend capability to describe itself . Video adaptation has been considered as a promising technique to tackle challenging problems in pervasive media applications . However , the style of video representation and description in current framework is too simple to accommodate diversified usages , especially for realtime application environment . In this paper , an intermediate video description termed intermedia is proposed , which is generated by off-line pre-processing the original video signal and includes abundant information of video content . Based on the learned models , dense videos are synthesized , concatenated and downsampled . A new generative model is then built on the remaining image samples for the final video synthesis . By exploiting the model structure , efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process . Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments : dimensionality reduction and reconstruction using synthetic time series , video synthesis using a dynamic texture database , and human motion synthesis , classification and tracking on a benchmark data set . In all experiments , the proposed approach provides superior performance . A new real-time infrared image processing system based on Field Programmable Gate Array(FPGA ) is proposed . The system implemented nonuniformity correction , image enhancement , and video synthesization , etc . By using parallel architecture and pipeline technique in FPGA , the system processing speed was as high as 50M × 12 bit/s . Furthermore , we can use the mouth interior and eye textures produced by our network to synthesize on-the-fly avatar animations for those regions . Our work produces state-of-the-art quality image and video synthesis , and is the first to our knowledge that is able to generate a dynamically textured avatar with a mouth interior , all from a single image . We present an extension of Cohen 's Wang Tiles to three dimensions : Wang Cubes . Cubes are filled with video or Poisson distributed points to perform realtime video synthesis or geometry placement . New methods for the estimation of MPEG‐4 facial animation parameters as well as scene illumination are proposed . Experiments for different applications demonstrate the potential of using facial animation techniques in video analysis and synthesis . A model‐based codec is presented that is able to encode head‐and‐shoulder video sequences at bit‐rates of about 1 kbit/s . Instead of learning synthesis from scratch , we introduce augmented inputs to compensate texture details and motion information . Specifically , a coarse-to-fine guidance scheme with a well-designed semantic loss is presented to improve the capability of video frame synthesis . As shown in the experiments , our proposed PMSN promises excellent quantitative results , visual effects , and generalization ability compared with traditional solutions . Experimental results , as reported in following , prove that this proposed system . Furthermore this scheme can be extended to deal with more general video or image analysis and synthesis systems . In order to develop Video Information Systems ( VIS ) such as digital video libraries , video-on-demand systems and video synthesis applications , we need to understand the semantics of video data , and have appropriate schemes to store , retrieve and present this data . In this paper , we have presented an integrated multi-model framework for designing VIS application that accommodates semantic representation and supports a variety of forms of content-based retrieval . We study the problem of video-to-video synthesis , whose goal is to learn a mapping function from an input source video ( e.g. , a sequence of semantic segmentation masks ) to an output photorealistic video that precisely depicts the content of the source video . While its image counterpart , the image-to-image synthesis problem , is a popular topic , the video-to-video synthesis problem is less explored in the literature . Without understanding temporal dynamics , directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality . In this work , we address the problem of semi-supervised video object segmentation , where the task is to segment a target object in every image of the video sequence , given a ground truth only in th ... Experimental results demonstrate that our model achieves superior empirical performance on video salient object detection . Moreover , an experiment on the extended application to unsupervised video object segmentation further demonstrates the generalization ability and stability of our proposed method . Unsupervised video object segmentation is proposed in this paper , using an adaptively trained neural network structure followed by a face and body detection scheme . The latter uses probabilistic modeling for applying the face and body detection task . We developed a real-time , high-quality video object segmentation algorithm for semi-supervised video segmentation . Its performance is on par with the most accurate , time-consuming online-learning model , while its speed is similar to the fastest template-matching method which has sub-optimal accuracy . The encoder-decoder based methods for semi-supervised video object segmentation ( Semi-VOS ) have received extensive attentions due to their superior performances . However , most of them have complex intermediate networks which generate strong specifiers , to be robust against challenging scenarios , and this is quite inefficient when dealing with relatively simple scenarios . Object segmentation in videos has been extensively investigated recent years . However , semi-supervised object segmentation in videos is still a challenging research topic as it is hard to modeling temporal information . Most of research treats video frames independence and lost the relationship between adjacent frames . However , existing data-driven approaches heavily rely on a large quantity of pixel-wise annotated video frames to deliver such promising results . In this paper , we address the semi-supervised video salient object detection task using pseudo-labels . Specifically , we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module . Segmentation of objects in an image is even more difficult and computationally expensive . In this paper an unsupervised object based image segmentation that is mean shift clustering approach will be studied . One of the most important step is pre-processed image by a standard mean shift based segmentation , which preserves desirable discontinuities present in the image and guarantees over segmentation in the image in their Output . One of the most challenging tasks in computer vision is to emulate human cognitive ability to extract the salient object in a scene . We tackle the task of unsupervised salient video object segmentation using boundary connectedness and space-time salient regions . First , boundary prior measure is used to separate salient regions detected in both space and time . In this work , we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects . First , we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions . Jointly with the eye tracking data , this use case provides a validation framework to assess the relevance of saliency extraction methods . By using our proposed saliency models for video coding purposes , we demonstrate substantial performances gains . For broadcasters , it is desirable to have processes capable of removing unwanted corruption in archive video material prior to broad-cast . In this paper a method of video restoration is described which specifically addresses the problem of video " scratching " . The concept of removing temporal redundancy is based on motion estimation . These facts make the decision , which algorithm to use , very important in video encoder development . Second part of this work is dedicated to motion estimation algorithms . The modelling involves the specification of synchronization constraints for tasks and the simulation of application behaviour . A concluding casestudy of video encoder development reveals that the approach is not only feasible but also brings a contribution to the design of the softwareintensive multimedia real-time systems . The importance of forest fire detecting was up to the destructive of forest fire , which can destroy resource safety and threaten human-living environment . The traditional forest fire monitoring and early warning methods are manly based on ground patrolling , watching tower , aerial patrolling , long-distance video monitoring , satellite monitoring , which are often inclined to miss the best time to extinguish the fire . This paper illustrates a real-time forest fire monitoring method based on the technology of WSN ( wireless sensor network ) and forest fire dangerous rating in comparison to the deficiency of traditional forest fire monitoring measures . A design schema of five-channel digital video optical fiber transmission system based on FPGA , which can accomplish long-distance video-monitoring , is brought forward . A non-compressed digital video optical fiber transmission system based on the TDM technology is designed . This is a review of a Specific application for video interconnection on single-mode optical fiber Over a 13.9 mi path , covering system design , aerial and underground plant construction , terminal equipment selection , and operating results . Both digital and analog circuits are used in the system , and the economics and performance of the two approaches are compared . The first problem of interconnection that need to be solved is to unify call signaling and video coding standard , so now , State Grid Cooperation is drafting enterprise criterion of Grid Video Surveillance System and Interfaces part 1:Technical Requirements to solve the troubles of systems interconnection . The article expatiate the construction project of video interconnection according reality of power enterprise . In this paper an interaction framework for AR enhanced video conferencing is presented . The goal is to provide a cheap and portable system based on a combination of commodity Kinect cameras and regular computer screens . We present AdaFrame , a framework that adaptively selects relevant frames on a per-input basis for fast video recognition . AdaFrame contains a Long Short-Term Memory network augmented with a global memory that provides context information for searching which frames to use over time . It is allows telephone calls to be made over digital computer network including the Internet . VOIP‟s main advantage is audio and video call communication . But some problems are create in VOIP system like spam calls . In view of the drastic increase of storage resources and transmission bandwidth requirement for high dynamic range ( HDR ) video compared to the traditional low dynamic range ( LDR ) video , we propose a dynamic rate distortion optimization algorithm based on visual perception for HDR Video encoding to improve the performance of high efficiency video coding ( HEVC ) Main 10 for coding HDR video . With the information of visual selective attention , we design a non-uniform distortion weight distribution strategy to different regions of interest and improve the conventional method of distortion calculation . A novel method for predicting the ratings of video content on the basis of a viewer 's behavior in a typical home environment is proposed . Using an input signal provided by a Kinect sensor , it identifies the presence of a viewer by extracting key point trajectories in video sequences of that viewer . Sequence - to - Sequence learning with deep neural networks ( herein , seq2seq ) has rapidly become a very useful and surprisingly general - purpose tool for natural language processing . In addition to demonstrating impressive results for machine translation , roughly the same model and training have also proven to be useful for sentence compression , parsing , and dialogue systems , and they additionally underlie other text generation applications , such as image or video captioning . The dominant approach to training a seq2seq system is as a conditional language model , with training maximizing the likelihood of each successive target word conditioned on the input sequence and the gold history of target words . Convolutional networks trained with large datasets of millions of images of thousands of subjects have shown remarkable capability of learning facial representations that are invariant to age , pose , illumination and expression ( A - PIE ) . These representations have shown strong performance for recognition of imagery and video in - the - wild in unconstrained datasets , with recent approaches demonstrating capabilities that exceed human performance on the well known Labeled Faces in the Wild dataset . The problem of face recognition may be described in terms of face verification and face identification . The SPDNet was able to outperform other methods . subsection : Results on Video - based Problem The results of our proposed methods , baseline method and the accuracies of other C3D and CNN - RNN models from are presented for context . section : Introduction Since the 2012 ImageNet competition winning entry by Krizhevsky et al , their network “ AlexNet ” has been successfully applied to a larger variety of computer vision tasks , for example to object - detection , segmentation , human pose estimation , video classification , object tracking , and superresolution . These successes spurred a new line of research that focused on finding higher performing convolutional neural networks . Today ’s computers , however , are fast enough for this , more than 60000 times faster than those of the early 90s . Carefully designed code for massively parallel graphics processing units ( GPUs normally used for video games ) allows for gaining an additional speedup factor of 50 - 100 over serial code for standard computers . Given enough labeled data , our networks do not need additional heuristics such as unsupervised pre - training or carefully prewired synapses .
Inspired by recent work in machine translation and object detection , we propose a new approach for video captioning which aims to incorporate Knowledge Base information with frame features of the video . We compare and analyze our approach with prior work and show that the large volumes information is available to generate video description . We experiment with our ideas on the S2VT model , and we demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks .
Temporal attention has been widely used in video description to adaptively focus on important frames . However , most existing methods based on temporal attention suffer from the problems of recognition error and detail missing , because only coarse frame-level global features are employed .
A digital video camera system that provides a video summary , using a method comprising the following steps . A step of capturing a video sequence of a scene using an image sensor , a video sequence step comprises a time sequence of image frames ; step of specifying the reference data that includes predetermined human form a digital video file step using video processing path for processing a captured video sequence to ; during the video sequence capture , by using the human recognition algorithm to identify a subset of an image frame including a predetermined human , the step of analyzing the captured image frame ; a step of forming a video summary containing fewer image frames than all of the image frames in the captured video sequence , video summary includes a predetermined human Features The storing step into the storage memory digital video file ; has been the subset of the image frame steps comprising at least partially the step storing a representation of the video summaries in the storage memory is .
Video summary is important for users to quickly understand the video content . In this paper , first , some selection principles of video content and an importance evaluation model of movie summary are analyzed .
Working with long temporal intervals ( up to 100 frames ) enables our system to significantly reduce annotation effort with respect to state of the art systems . Even though the segmentation results are less than perfect , they are obtained efficiently and can be used in weakly supervised learning from video or for video content description . We do not rely on a discriminative object appearance model and allow extracting multiple foreground objects together , saving user time if more than one object is present .
Specially , aiming at the solution of the deepening video application on intelligent transportation and public security , the video structured description technique on android platform was utilized to execute the scene description task and implement the recognition task such as face recognition and vehicle recognition . The schema of video structured description on mobile computing architecture was figured out and the synthesis searching framework was introduced by utilized the semantic web search engine . As our experiments proved , the OpenCV for Android based video analysis and event detection is eligible for the present ARM-based hardware configuration .
CNNs are pretrained on object and/or action recognition tasks and used to encode video-level features . The decoder is then optimised on such static features to generate the video 's description . This disjoint setup is arguably sub-optimal for input ( video ) to output ( description ) mapping .
CNNs are pretrained on object and/or action recognition tasks and used to encode video-level features . The decoder is then optimised on such static features to generate the video 's description . This disjoint setup is arguably sub-optimal for input ( video ) to output ( description ) mapping .
Classical approaches were followed by a very short era of statistical methods that were soon replaced with deep learning , the current state-of-the-art in video description . Our survey shows that despite the fast-paced developments , video description research is still in its infancy due to the following reasons : Analysis of video description models is challenging , because it is difficult to ascertain the contributions towards accuracy or errors of the visual features and the adopted language model in the final description . Existing datasets neither contain adequate visual diversity nor complexity of linguistic structures .
In this paper we propose a framework for development of video analysis and description systems , in an easy and interactive way . Due to the architecture design the developed software can run on different operating systems and on distributed environments .
This step uses optical flow along with dense speed-up-robust-features ( SURF ) in order to detect and track moving humans in moving fields of view . The video description step is based on a fusion process that combines displacement and spatio-temporal descriptors . Experiments are carried out on the big data-set UCF-101 .
The theory of ontology is applied to the video feature description together with its interface in MPEG-7 and video information based on semantic hierarchy , to build up a video semantic description model . This model makes use of the RDFS and Owl 's good capability to describe the semantic information and self-extend capability to describe itself .
Video adaptation has been considered as a promising technique to tackle challenging problems in pervasive media applications . However , the style of video representation and description in current framework is too simple to accommodate diversified usages , especially for realtime application environment . In this paper , an intermediate video description termed intermedia is proposed , which is generated by off-line pre-processing the original video signal and includes abundant information of video content .
Based on the learned models , dense videos are synthesized , concatenated and downsampled . A new generative model is then built on the remaining image samples for the final video synthesis .
By exploiting the model structure , efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process . Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments : dimensionality reduction and reconstruction using synthetic time series , video synthesis using a dynamic texture database , and human motion synthesis , classification and tracking on a benchmark data set . In all experiments , the proposed approach provides superior performance .
A new real-time infrared image processing system based on Field Programmable Gate Array(FPGA ) is proposed . The system implemented nonuniformity correction , image enhancement , and video synthesization , etc . By using parallel architecture and pipeline technique in FPGA , the system processing speed was as high as 50M × 12 bit/s .
Furthermore , we can use the mouth interior and eye textures produced by our network to synthesize on-the-fly avatar animations for those regions . Our work produces state-of-the-art quality image and video synthesis , and is the first to our knowledge that is able to generate a dynamically textured avatar with a mouth interior , all from a single image .
We present an extension of Cohen 's Wang Tiles to three dimensions : Wang Cubes . Cubes are filled with video or Poisson distributed points to perform realtime video synthesis or geometry placement .
New methods for the estimation of MPEG‐4 facial animation parameters as well as scene illumination are proposed . Experiments for different applications demonstrate the potential of using facial animation techniques in video analysis and synthesis . A model‐based codec is presented that is able to encode head‐and‐shoulder video sequences at bit‐rates of about 1 kbit/s .
Instead of learning synthesis from scratch , we introduce augmented inputs to compensate texture details and motion information . Specifically , a coarse-to-fine guidance scheme with a well-designed semantic loss is presented to improve the capability of video frame synthesis . As shown in the experiments , our proposed PMSN promises excellent quantitative results , visual effects , and generalization ability compared with traditional solutions .
Experimental results , as reported in following , prove that this proposed system . Furthermore this scheme can be extended to deal with more general video or image analysis and synthesis systems .
In order to develop Video Information Systems ( VIS ) such as digital video libraries , video-on-demand systems and video synthesis applications , we need to understand the semantics of video data , and have appropriate schemes to store , retrieve and present this data . In this paper , we have presented an integrated multi-model framework for designing VIS application that accommodates semantic representation and supports a variety of forms of content-based retrieval .
We study the problem of video-to-video synthesis , whose goal is to learn a mapping function from an input source video ( e.g. , a sequence of semantic segmentation masks ) to an output photorealistic video that precisely depicts the content of the source video . While its image counterpart , the image-to-image synthesis problem , is a popular topic , the video-to-video synthesis problem is less explored in the literature . Without understanding temporal dynamics , directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality .
In this work , we address the problem of semi-supervised video object segmentation , where the task is to segment a target object in every image of the video sequence , given a ground truth only in th ...
Experimental results demonstrate that our model achieves superior empirical performance on video salient object detection . Moreover , an experiment on the extended application to unsupervised video object segmentation further demonstrates the generalization ability and stability of our proposed method .
Unsupervised video object segmentation is proposed in this paper , using an adaptively trained neural network structure followed by a face and body detection scheme . The latter uses probabilistic modeling for applying the face and body detection task .
We developed a real-time , high-quality video object segmentation algorithm for semi-supervised video segmentation . Its performance is on par with the most accurate , time-consuming online-learning model , while its speed is similar to the fastest template-matching method which has sub-optimal accuracy .
The encoder-decoder based methods for semi-supervised video object segmentation ( Semi-VOS ) have received extensive attentions due to their superior performances . However , most of them have complex intermediate networks which generate strong specifiers , to be robust against challenging scenarios , and this is quite inefficient when dealing with relatively simple scenarios .
Object segmentation in videos has been extensively investigated recent years . However , semi-supervised object segmentation in videos is still a challenging research topic as it is hard to modeling temporal information . Most of research treats video frames independence and lost the relationship between adjacent frames .
However , existing data-driven approaches heavily rely on a large quantity of pixel-wise annotated video frames to deliver such promising results . In this paper , we address the semi-supervised video salient object detection task using pseudo-labels . Specifically , we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module .
Segmentation of objects in an image is even more difficult and computationally expensive . In this paper an unsupervised object based image segmentation that is mean shift clustering approach will be studied . One of the most important step is pre-processed image by a standard mean shift based segmentation , which preserves desirable discontinuities present in the image and guarantees over segmentation in the image in their Output .
One of the most challenging tasks in computer vision is to emulate human cognitive ability to extract the salient object in a scene . We tackle the task of unsupervised salient video object segmentation using boundary connectedness and space-time salient regions . First , boundary prior measure is used to separate salient regions detected in both space and time .
In this work , we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects . First , we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions .
Jointly with the eye tracking data , this use case provides a validation framework to assess the relevance of saliency extraction methods . By using our proposed saliency models for video coding purposes , we demonstrate substantial performances gains .
For broadcasters , it is desirable to have processes capable of removing unwanted corruption in archive video material prior to broad-cast . In this paper a method of video restoration is described which specifically addresses the problem of video " scratching " .
The concept of removing temporal redundancy is based on motion estimation . These facts make the decision , which algorithm to use , very important in video encoder development . Second part of this work is dedicated to motion estimation algorithms .
The modelling involves the specification of synchronization constraints for tasks and the simulation of application behaviour . A concluding casestudy of video encoder development reveals that the approach is not only feasible but also brings a contribution to the design of the softwareintensive multimedia real-time systems .
The importance of forest fire detecting was up to the destructive of forest fire , which can destroy resource safety and threaten human-living environment . The traditional forest fire monitoring and early warning methods are manly based on ground patrolling , watching tower , aerial patrolling , long-distance video monitoring , satellite monitoring , which are often inclined to miss the best time to extinguish the fire . This paper illustrates a real-time forest fire monitoring method based on the technology of WSN ( wireless sensor network ) and forest fire dangerous rating in comparison to the deficiency of traditional forest fire monitoring measures .
A design schema of five-channel digital video optical fiber transmission system based on FPGA , which can accomplish long-distance video-monitoring , is brought forward . A non-compressed digital video optical fiber transmission system based on the TDM technology is designed .
This is a review of a Specific application for video interconnection on single-mode optical fiber Over a 13.9 mi path , covering system design , aerial and underground plant construction , terminal equipment selection , and operating results . Both digital and analog circuits are used in the system , and the economics and performance of the two approaches are compared .
The first problem of interconnection that need to be solved is to unify call signaling and video coding standard , so now , State Grid Cooperation is drafting enterprise criterion of Grid Video Surveillance System and Interfaces part 1:Technical Requirements to solve the troubles of systems interconnection . The article expatiate the construction project of video interconnection according reality of power enterprise .
In this paper an interaction framework for AR enhanced video conferencing is presented . The goal is to provide a cheap and portable system based on a combination of commodity Kinect cameras and regular computer screens .
We present AdaFrame , a framework that adaptively selects relevant frames on a per-input basis for fast video recognition . AdaFrame contains a Long Short-Term Memory network augmented with a global memory that provides context information for searching which frames to use over time .
It is allows telephone calls to be made over digital computer network including the Internet . VOIP‟s main advantage is audio and video call communication . But some problems are create in VOIP system like spam calls .
In view of the drastic increase of storage resources and transmission bandwidth requirement for high dynamic range ( HDR ) video compared to the traditional low dynamic range ( LDR ) video , we propose a dynamic rate distortion optimization algorithm based on visual perception for HDR Video encoding to improve the performance of high efficiency video coding ( HEVC ) Main 10 for coding HDR video . With the information of visual selective attention , we design a non-uniform distortion weight distribution strategy to different regions of interest and improve the conventional method of distortion calculation .
A novel method for predicting the ratings of video content on the basis of a viewer 's behavior in a typical home environment is proposed . Using an input signal provided by a Kinect sensor , it identifies the presence of a viewer by extracting key point trajectories in video sequences of that viewer .
Sequence - to - Sequence learning with deep neural networks ( herein , seq2seq ) has rapidly become a very useful and surprisingly general - purpose tool for natural language processing . In addition to demonstrating impressive results for machine translation , roughly the same model and training have also proven to be useful for sentence compression , parsing , and dialogue systems , and they additionally underlie other text generation applications , such as image or video captioning . The dominant approach to training a seq2seq system is as a conditional language model , with training maximizing the likelihood of each successive target word conditioned on the input sequence and the gold history of target words .
Convolutional networks trained with large datasets of millions of images of thousands of subjects have shown remarkable capability of learning facial representations that are invariant to age , pose , illumination and expression ( A - PIE ) . These representations have shown strong performance for recognition of imagery and video in - the - wild in unconstrained datasets , with recent approaches demonstrating capabilities that exceed human performance on the well known Labeled Faces in the Wild dataset . The problem of face recognition may be described in terms of face verification and face identification .
The SPDNet was able to outperform other methods . subsection : Results on Video - based Problem The results of our proposed methods , baseline method and the accuracies of other C3D and CNN - RNN models from are presented for context .
section : Introduction Since the 2012 ImageNet competition winning entry by Krizhevsky et al , their network “ AlexNet ” has been successfully applied to a larger variety of computer vision tasks , for example to object - detection , segmentation , human pose estimation , video classification , object tracking , and superresolution . These successes spurred a new line of research that focused on finding higher performing convolutional neural networks .
Today ’s computers , however , are fast enough for this , more than 60000 times faster than those of the early 90s . Carefully designed code for massively parallel graphics processing units ( GPUs normally used for video games ) allows for gaining an additional speedup factor of 50 - 100 over serial code for standard computers . Given enough labeled data , our networks do not need additional heuristics such as unsupervised pre - training or carefully prewired synapses .
Video Captioning with Semantic Information from the Knowledge Base Video Description with Spatial-Temporal Attention Video summary containing a predetermined human A Movie Summary Generation System Interactive video segmentation using occlusion boundaries and temporally coherent superpixels The light-weighting realization of video structured description on android-based terminals An End-to-End Baseline for Video Captioning End-to-End Video Captioning Video Description Generic Framework for Video Analysis Spatio-temporal action localization for human action recognition in large dataset THE MPEG-7 VIDEO SEMANTIC DESCRIPTION MODEL BASED ON ONTOLOGY Intermediate description for multiple video adaptation Synthesizing a talking mouth Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series Real-time image processing system for IRFPA based on FPGA paGAN: real-time avatars using dynamic textures Wang cubes for video synthesis and geometry placement MPEG‐4 facial animation in video analysis and synthesis Augmented Coarse-to-Fine Video Frame Synthesis with Semantic Loss Virtual face rendering based on gradient features in VLBR networks A Multi-Model Framework for Video Information Systems Video-to-Video Synthesis Discriminative Learning and Target Attention for the 2019 DAVIS Challenge onVideo Object Segmentation Attention Embedded Spatio-Temporal Network for Video Salient Object Detection Object based coding of video sequences at low bit rates using adaptively trained neural networks Fast Video Object Segmentation using the Global Context Module CRVOS: Clue Refining Network for Video Object Segmentation Semi-supervised Video Object Segmentation with Recurrent Neural Network Semi-Supervised Video Salient Object Detection Using Pseudo-Labels Mean shift technique for image segmentation and Modified Canny Edge Detection Algorithm for circle detection Boundary connectedness-based salient video object segmentation Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks Visual Attention Modelling in a 3D Context Application of Non-Linear Image Processing: Digital Video Archive Restoration A stochastic algorithm for block-based motion compensation in video coding Predicting real-time properties of component-based applications A Forest Fire Warning Method Based on Fire Dangerous Rating Study of Digital Video Optical Fiber Transmission System Based on FPGA Optical Fiber Supertrunking-A Performance Report on a Real-World System Unified communication and exchanging platform of video monitoring systems used in ultra high voltage gird Interface design for an inexpensive hands-free collaborative videoconferencing system AdaFrame: Adaptive Frame Selection for Fast Video Recognition Enhance approach for handling spam calls in VOIP scenario Visual perception based rate distortion optimization method for high dynamic range video coding An Estimator for Rating Video Contents on the Basis of a Viewer's Behavior in Typical Home Environments Sequence-to-Sequence Learning as Beam-Search Optimization Template adaptation for face verification and identification Covariance Pooling for Facial Expression Recognition Rethinking the Inception Architecture for Computer Vision Multi-column deep neural networks for image classification
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2017 2017 2012 2017 2014 2013 2019 2019 2020 2002 2015 2007 2009 2010 2007 2006 2019 2004 2003 2018 2002 1999 2018 2019 2019 1999 2020 2020 2019 2019 2014 2018 2018 2015 1997 2008 2005 2015 2007 1986 2011 2012 2018 2018 2018 2013 2016 2018 2018 2016 2012
ICBK ACM Multimedia DSC WACV ICCV Workshops ICVGIP ICCV SIGGRAPH Posters PRCV (1) VCIP DS-8 ECCV (3) ICIP (1) ISMAR SITIS EMNLP Image Vis. Comput. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012 IEEE Conference on Computer Vision and Pattern Recognition
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