The experimental results show that the proposed method of enhancement reduces the time and memory requirements , and enables the use of all training data in the k-means clustering algorithm . The evaluation of accuracy of action classification on two popular datasets ( KTH and Weizmann ) has been performed . In addition , the proposed method improves the human activity recognition accuracy by 5.57 % on the KTH dataset using the same detector , descriptor , and classifier . Finally , we collect upper ranked shots as training data for the action concept . We demonstrate that with action models trained by our data , we can obtain promising precision rates in the task of action classification while offering the advantage of a fully automatic , scalable learning . Experiment results on UCF11 , a challenging action dataset , show the effectiveness of our method . In this paper , we determine whether incomplete videos that are often discarded carry useful information for action recognition , and if so , how one can represent such mixed collection of video data ( complete versus incomplete , and labeled versus unlabeled ) in a unified manner . We propose a novel framework to handle incomplete videos in action classification , and make three main contributions : ( 1 ) We cast the action classification problem for a mixture of complete and incomplete data as a semi-supervised learning problem of labeled and unlabeled data . ( 2 ) We introduce a two-step approach to convert the input mixed data into a uniform compact representation . Build the personalized ECG templates , apply factor graph and max-sum algorithm for precise template matching , and realize the feature extraction and representation of ECG data . II . Use the action sensor data for action classification and identification . As an important factor of health monitoring , body movements are categorized as resting , walking , going upstairs and downstairs , flipping and sudden change . Our conclusion generalizes to datasets with very different properties . When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating , our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks ( Kinetics , Something-something , UCF101 and HMDB ) , as well as two action detection ( localization ) benchmarks ( JHMDB and UCF101 - 24 ) . Action classification and recognition is a challenging research area that has significant applications in computer vision domain including robotics , video surveillance , human-computer interaction and multimedia retrieval . Action classification domain uses a large variety of approaches . This paper proposes a new approach for video actions classification based on extension of Krawtchouk moments in spatio-temporal domain . We demonstrate the improvement in optical flow estimation on the real-world KITTI benchmark . Additionally , we test the optical flow indirectly in an action classification scenario . As a side product of this work , we report significant improvements over state-of-the-art in the task of next-frame prediction . The main aim of action recognition is an automatic analysis of various actions from video data . Major video analysis system includes action detection and classification , action tracking , recognizing actions and behavior understanding . Even though , traditional methods have achieved greater success on several human actions . In this paper , we propose a method that enables robots to comprehend instructions spoken by a human user in his/her natural language . The proposed method combines action-type classification , which is based on a support vector machine , and slot extraction , which is based on conditional random fields , both of which are required in order for a robot to execute an action . Further , by considering the co-occurrence relationship between the action type and the slots along with the speech recognition score , the proposed method can avoid degradation of the robot ’s comprehension accuracy in noisy environments , where inaccurate speech recognition can be problematic . Video Question Answering ( Video QA ) is one of the important and challenging problems in multimedia and computer vision research . In this paper , we propose a novel framework , called initialized frame attention networks ( IFAN ) . Question-answering ( QA ) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings . Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos . We develop a video-story learning model , i.e. Deep Embedded Memory Networks ( DEMN ) , to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data . Extensive experimental results show that our method outperforms previous video saliency models in a large margin , with a real-time speed of 20 fps on a single GPU . With unsupervised video object segmentation as an example application , the proposed model ( with a CRF-based post-process ) achieves state-of-the-art results on two popular benchmarks , well demonstrating its superior performance and high applicability . We propose a novel spatiotemporal graphical model for unsupervised video object segmentation . The core of our model is a layered-CRF ( conditional random field ) that contains two layers , i.e. , pixel layer and supervoxel layer . The key observation here is that while the most expressive model often ends up a winner by the end of the iterative learning of model parameters , early iterations are dominated by simpler model components , and upon convergence , the free energy is lower than the ones reached by switching on all the most complex components from the beginning of the learning . We illustrate the performance of this approach on the unsupervised video segmentation task . This paper investigates how to perform robust and efficient unsupervised video segmentation while suppressing the effects of data noises and/or corruptions . We propose a general algorithm , called Sub-Optimal Low-rank Decomposition ( SOLD ) , which pursues the low-rank representation for video segmentation . We present an approach for unsupervised object segmentation in unconstrained videos . Driven by the latest progress in this field , we argue that segmentation performance can be largely improved by aggregating the results generated by state-of-the-art algorithms . Motivated by the emerging video coding standard MPEG-4 , this paper proposes a solution to address the problems of unsupervised object segmentation in images and video sequences , using color information . Although the human visual system is very sensitive to edge information , image segmentation is one of the most fundamental and yet complex tasks in image processing . We investigate the problem of strictly unsupervised video object segmentation , i.e. , the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset . We find foreground objects in low-level vision data using a John Tukey-inspired measure of " outlierness . " Direct processing of JPEG images based on DCT coefficients could avoid computationally intensive full decoding and large memory storage . In this paper , we exploit the inherent information extracted from DCT coefficients to achieve unsupervised segmentation of image objects . First , a madmum entropy fuzzy clustering ( MEFC ) algorithm is proposed to achieve a coarse segmentation based on DCTDC coefficients . Direct processing of JPEG images based on DCT coefficients could avoid computationally intensive full decoding and large memory storage . In this paper , we exploit the inherent information extracted from DCT coefficients to achieve unsupervised segmentation of image objects . First , a maximum entropy fuzzy clustering ( MEFC ) algorithm is proposed to achieve a coarse segmentation based on DCT-DC coefficients . Point trajectories have emerged as a powerful means to obtain high quality and fully unsupervised segmentation of objects in video shots . They can exploit the long term motion difference between objects , but they tend to be sparse due to computational reasons and the difficulty in estimating motion in homogeneous areas . This paper presents a novel method for unsupervised segmentation of objects with large displacements in high speed video sequences . Our general framework introduces a new foreground object predicting method that finds object hypotheses by encoding both spatial and temporal features via a semantic motion signature scheme . We propose a simple , fast , and effective algorithm for locating and segmenting salient objects by analysing image collections . As a key novelty , we introduce group saliency to achieve superior unsupervised salient object segmentation by extracting salient objects ( in collections of pre-filtered images ) that maximize between-image similarities and within-image distinctness . To evaluate our method , we construct a large benchmark dataset consisting of 15 K images across multiple categories with 6000 + pixel-accurate ground truth annotations for salient object regions where applicable . In addition , a particularly effective novel neural paradigm is introduced and described . The new approach is based on a proper quad-tree segmentation of video frames and is capable to yield a considerable improvement with respect to existing standards in high quality video compression . Experimental tests are described to demonstrate the efficacy of the proposed solution . Few studies have examined the impact of video laryngoscopy ( VL ) on operating room efficiency . We hypothesized that VL reduces anesthesia control time ( ACT ) , a metric of anesthesia efficiency , compared with fiberoptic intubation ( FOI ) in potentially difficult airways , but that direct laryngoscopy ( DL ) remains more efficient in routine cases . Medical trainees require sufficient practice to gain the experience and confidence needed to safely and reliably perform endotracheal intubations . While video laryngoscopy has been used to provide an advanced glottic view that can reduce intubation failure , prevent prolonged intubation time , and reduce repeated intubation attempts , most current devices require visualization on external monitors , disrupting the direct line-of-sight view . These devices also present a deep intra-oral view of the airway that may not be visible during a typical unassisted intubation attempt . Our experimental results shows that our saving of memory bandwidth can be 23 % more than what the best design can achieve by exploring the intra-partition data reuse only . The proposed design methodology has been implemented , and the result shows that the proposed interpolation architecture is the most compact design among the literatures which can perform the real-time H.264 video decoding with resolution up to 1920 × 1088 high-definition television standard . The proposed interpolator can be applied to the dedicated H.264 hardware codec design for various consumer devices . Abstract Automated and intelligent video surveillance systems play important role in current home care and facilities security applications . Among many research problems is graphical visualization of semantic messages to the human operator that he can percept information in more natural way . The 360-degree video is projected to 2-D formats using various projection methods for efficient compression . As a necessary part of general-video compression , rate control is also indispensable for the projected 360-degree video compression . However , the current rate control algorithm has not been optimized for the 360-degree video compression yet . Therefore , it is essential to apply efficient compression techniques for compressing surveillance video . The techniques used for the general video compression may not be the efficient technique for the compression of surveillance video because of the use of static camera as compared to moving camera in general purpose videos . Generally surveillance video consist of multiple objects , smaller in size as compared to the background and they have frequents occlusion with each other . This work is based on the study and development of methodologies for objective evaluation of video unidirectional transmission systems . Particularly , the video compression systems ( called codecs ) are worked according to MPEG-2 standard , but they can be applied to evaluate other compression systems . For modeling the emotional view of videos , we incorporate visual sentiment , video content , and text comments into the learning of the embedding space . On a newly collected dataset with over 102 K videos and 10.6 M comments , we demonstrate that our approach outperforms several state-of-the-art methods and achieves human-level video commenting . This paper designs IP video monitoring based on campus network for special university environment . It not only strengthens the management of campus security , but also improves the modern management of school . The synopsis described the line processing system structure of the FPGA + DSP , and take this as the foundation structure , design a kind of airplane electronics to probe the video frequency processing system , and explain that video frequency processing system of basic carry out a method . As one of the most important applications in peer-to-peer ( P2P ) networks , the video-on-demand ( VoD ) system freely supports video cassette recorder ( VCR ) operation for users . However , the users may experience significant playback delay after frequent VCR operations in the VoD system , which will affect the quality of experience ( QoE ) of the users .
The experimental results show that the proposed method of enhancement reduces the time and memory requirements , and enables the use of all training data in the k-means clustering algorithm . The evaluation of accuracy of action classification on two popular datasets ( KTH and Weizmann ) has been performed . In addition , the proposed method improves the human activity recognition accuracy by 5.57 % on the KTH dataset using the same detector , descriptor , and classifier .
Finally , we collect upper ranked shots as training data for the action concept . We demonstrate that with action models trained by our data , we can obtain promising precision rates in the task of action classification while offering the advantage of a fully automatic , scalable learning . Experiment results on UCF11 , a challenging action dataset , show the effectiveness of our method .
In this paper , we determine whether incomplete videos that are often discarded carry useful information for action recognition , and if so , how one can represent such mixed collection of video data ( complete versus incomplete , and labeled versus unlabeled ) in a unified manner . We propose a novel framework to handle incomplete videos in action classification , and make three main contributions : ( 1 ) We cast the action classification problem for a mixture of complete and incomplete data as a semi-supervised learning problem of labeled and unlabeled data . ( 2 ) We introduce a two-step approach to convert the input mixed data into a uniform compact representation .
Build the personalized ECG templates , apply factor graph and max-sum algorithm for precise template matching , and realize the feature extraction and representation of ECG data . II . Use the action sensor data for action classification and identification . As an important factor of health monitoring , body movements are categorized as resting , walking , going upstairs and downstairs , flipping and sudden change .
Our conclusion generalizes to datasets with very different properties . When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating , our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks ( Kinetics , Something-something , UCF101 and HMDB ) , as well as two action detection ( localization ) benchmarks ( JHMDB and UCF101 - 24 ) .
Action classification and recognition is a challenging research area that has significant applications in computer vision domain including robotics , video surveillance , human-computer interaction and multimedia retrieval . Action classification domain uses a large variety of approaches . This paper proposes a new approach for video actions classification based on extension of Krawtchouk moments in spatio-temporal domain .
We demonstrate the improvement in optical flow estimation on the real-world KITTI benchmark . Additionally , we test the optical flow indirectly in an action classification scenario . As a side product of this work , we report significant improvements over state-of-the-art in the task of next-frame prediction .
The main aim of action recognition is an automatic analysis of various actions from video data . Major video analysis system includes action detection and classification , action tracking , recognizing actions and behavior understanding . Even though , traditional methods have achieved greater success on several human actions .
In this paper , we propose a method that enables robots to comprehend instructions spoken by a human user in his/her natural language . The proposed method combines action-type classification , which is based on a support vector machine , and slot extraction , which is based on conditional random fields , both of which are required in order for a robot to execute an action . Further , by considering the co-occurrence relationship between the action type and the slots along with the speech recognition score , the proposed method can avoid degradation of the robot ’s comprehension accuracy in noisy environments , where inaccurate speech recognition can be problematic .
Video Question Answering ( Video QA ) is one of the important and challenging problems in multimedia and computer vision research . In this paper , we propose a novel framework , called initialized frame attention networks ( IFAN ) .
Question-answering ( QA ) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings . Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos . We develop a video-story learning model , i.e. Deep Embedded Memory Networks ( DEMN ) , to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data .
Extensive experimental results show that our method outperforms previous video saliency models in a large margin , with a real-time speed of 20 fps on a single GPU . With unsupervised video object segmentation as an example application , the proposed model ( with a CRF-based post-process ) achieves state-of-the-art results on two popular benchmarks , well demonstrating its superior performance and high applicability .
We propose a novel spatiotemporal graphical model for unsupervised video object segmentation . The core of our model is a layered-CRF ( conditional random field ) that contains two layers , i.e. , pixel layer and supervoxel layer .
The key observation here is that while the most expressive model often ends up a winner by the end of the iterative learning of model parameters , early iterations are dominated by simpler model components , and upon convergence , the free energy is lower than the ones reached by switching on all the most complex components from the beginning of the learning . We illustrate the performance of this approach on the unsupervised video segmentation task .
This paper investigates how to perform robust and efficient unsupervised video segmentation while suppressing the effects of data noises and/or corruptions . We propose a general algorithm , called Sub-Optimal Low-rank Decomposition ( SOLD ) , which pursues the low-rank representation for video segmentation .
We present an approach for unsupervised object segmentation in unconstrained videos . Driven by the latest progress in this field , we argue that segmentation performance can be largely improved by aggregating the results generated by state-of-the-art algorithms .
Motivated by the emerging video coding standard MPEG-4 , this paper proposes a solution to address the problems of unsupervised object segmentation in images and video sequences , using color information . Although the human visual system is very sensitive to edge information , image segmentation is one of the most fundamental and yet complex tasks in image processing .
We investigate the problem of strictly unsupervised video object segmentation , i.e. , the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset . We find foreground objects in low-level vision data using a John Tukey-inspired measure of " outlierness . "
Direct processing of JPEG images based on DCT coefficients could avoid computationally intensive full decoding and large memory storage . In this paper , we exploit the inherent information extracted from DCT coefficients to achieve unsupervised segmentation of image objects . First , a madmum entropy fuzzy clustering ( MEFC ) algorithm is proposed to achieve a coarse segmentation based on DCTDC coefficients .
Direct processing of JPEG images based on DCT coefficients could avoid computationally intensive full decoding and large memory storage . In this paper , we exploit the inherent information extracted from DCT coefficients to achieve unsupervised segmentation of image objects . First , a maximum entropy fuzzy clustering ( MEFC ) algorithm is proposed to achieve a coarse segmentation based on DCT-DC coefficients .
Point trajectories have emerged as a powerful means to obtain high quality and fully unsupervised segmentation of objects in video shots . They can exploit the long term motion difference between objects , but they tend to be sparse due to computational reasons and the difficulty in estimating motion in homogeneous areas .
This paper presents a novel method for unsupervised segmentation of objects with large displacements in high speed video sequences . Our general framework introduces a new foreground object predicting method that finds object hypotheses by encoding both spatial and temporal features via a semantic motion signature scheme .
We propose a simple , fast , and effective algorithm for locating and segmenting salient objects by analysing image collections . As a key novelty , we introduce group saliency to achieve superior unsupervised salient object segmentation by extracting salient objects ( in collections of pre-filtered images ) that maximize between-image similarities and within-image distinctness . To evaluate our method , we construct a large benchmark dataset consisting of 15 K images across multiple categories with 6000 + pixel-accurate ground truth annotations for salient object regions where applicable .
In addition , a particularly effective novel neural paradigm is introduced and described . The new approach is based on a proper quad-tree segmentation of video frames and is capable to yield a considerable improvement with respect to existing standards in high quality video compression . Experimental tests are described to demonstrate the efficacy of the proposed solution .
Few studies have examined the impact of video laryngoscopy ( VL ) on operating room efficiency . We hypothesized that VL reduces anesthesia control time ( ACT ) , a metric of anesthesia efficiency , compared with fiberoptic intubation ( FOI ) in potentially difficult airways , but that direct laryngoscopy ( DL ) remains more efficient in routine cases .
Medical trainees require sufficient practice to gain the experience and confidence needed to safely and reliably perform endotracheal intubations . While video laryngoscopy has been used to provide an advanced glottic view that can reduce intubation failure , prevent prolonged intubation time , and reduce repeated intubation attempts , most current devices require visualization on external monitors , disrupting the direct line-of-sight view . These devices also present a deep intra-oral view of the airway that may not be visible during a typical unassisted intubation attempt .
Our experimental results shows that our saving of memory bandwidth can be 23 % more than what the best design can achieve by exploring the intra-partition data reuse only . The proposed design methodology has been implemented , and the result shows that the proposed interpolation architecture is the most compact design among the literatures which can perform the real-time H.264 video decoding with resolution up to 1920 × 1088 high-definition television standard . The proposed interpolator can be applied to the dedicated H.264 hardware codec design for various consumer devices .
Abstract Automated and intelligent video surveillance systems play important role in current home care and facilities security applications . Among many research problems is graphical visualization of semantic messages to the human operator that he can percept information in more natural way .
The 360-degree video is projected to 2-D formats using various projection methods for efficient compression . As a necessary part of general-video compression , rate control is also indispensable for the projected 360-degree video compression . However , the current rate control algorithm has not been optimized for the 360-degree video compression yet .
Therefore , it is essential to apply efficient compression techniques for compressing surveillance video . The techniques used for the general video compression may not be the efficient technique for the compression of surveillance video because of the use of static camera as compared to moving camera in general purpose videos . Generally surveillance video consist of multiple objects , smaller in size as compared to the background and they have frequents occlusion with each other .
This work is based on the study and development of methodologies for objective evaluation of video unidirectional transmission systems . Particularly , the video compression systems ( called codecs ) are worked according to MPEG-2 standard , but they can be applied to evaluate other compression systems .
For modeling the emotional view of videos , we incorporate visual sentiment , video content , and text comments into the learning of the embedding space . On a newly collected dataset with over 102 K videos and 10.6 M comments , we demonstrate that our approach outperforms several state-of-the-art methods and achieves human-level video commenting .
This paper designs IP video monitoring based on campus network for special university environment . It not only strengthens the management of campus security , but also improves the modern management of school .
The synopsis described the line processing system structure of the FPGA + DSP , and take this as the foundation structure , design a kind of airplane electronics to probe the video frequency processing system , and explain that video frequency processing system of basic carry out a method .
As one of the most important applications in peer-to-peer ( P2P ) networks , the video-on-demand ( VoD ) system freely supports video cassette recorder ( VCR ) operation for users . However , the users may experience significant playback delay after frequent VCR operations in the VoD system , which will affect the quality of experience ( QoE ) of the users .
Enhanced bag of words using multilevel k-means for human activity recognition Automatic Construction of Action Datasets Using Web Videos with Density-Based Cluster Analysis and Outlier Detection Should we discard sparse or incomplete videos? Smart assisted diagnosis solution with multi-sensor Holter Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification Video Action Classification: A New Approach Combining Spatio-temporal Krawtchouk Moments and Laplacian Eigenmaps Hybrid Learning of Optical Flow and Next Frame Prediction to Boost Optical Flow in the Wild Study on Recent Approaches for Human Action Recognition in Real Time Robust comprehension of natural language instructions by a domestic service robot Initialized Frame Attention Networks for Video Question Answering DeepStory: Video Story QA by Deep Embedded Memory Networks Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection Unsupervised video object segmentation by spatiotemporal graphical model Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video SOLD: Sub-optimal low-rank decomposition for efficient video segmentation Video object segmentation aggregation Content-based object segmentation in video sequences Tukey-Inspired Video Object Segmentation UNSUPERVISED IMAGE OBJECT SEGMENTATION OVER COMPRESSED DOMAIN How-Lung Eng and Kai-Kuang Ma* School of Electrical and Electronic Engineering Unsupervised image object segmentation over compressed domain Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions Semantic Motion Signature for Segmentation of High Speed Large Displacement Objects SalientShape: group saliency in image collections Video Compression by Neural Networks The Impact of Airway Technique on Anesthesia Control Time Augmented Reality (AR) Assisted Laryngoscopy for Endotracheal Intubation Training An Efficient Design of H.264 Inter Interpolator with Bandwidth Optimization Ray-Tracing-Based Event Detection and 3D Visualization for Automated Video Surveillance System $\lambda$-Domain Perceptual Rate Control for 360-Degree Video Compression Object Based Fast Motion Estimation and Compensation Algorithm for Surveillance Video Compression Avaliação objetiva de codecs de video Share-and-Chat: Achieving Human-Level Video Commenting by Search and Multi-View Embedding Design of IP Video Monitoring Based on Campus Network Probe Video Frequency Processing System Based on a Certain Airplane Electronics of the FPGA+DSP Line Structure Data-Prefetching Scheme Based on Playback Delay and Positioning Satisfaction in Peer-To-Peer Video-On-Demand System
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2016 2015 2014 2017 2018 2011 2017 2015 2016 2017 2017 2018 2017 2006 2015 2016 1998 2018 2000 2000 2011 2017 2014 2005 2019 2019 2008 2015 2020 2014 2002 2016 2012 2010 2018
PSIVT ICIP ECCV (15) SITIS ICIMCS ECCV (11) CVPR (1) CVPR ICME ICIP ICCV HCI (10) ACM Multimedia
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