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2007 IEEE Workshop on Motion and Video Computing (WMVC'07)最新文献

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Human Limb Delineation and Joint Position Recovery Using Localized Boundary Models 基于局部边界模型的人体肢体描绘和关节位置恢复
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-23 DOI: 10.1109/WMVC.2007.18
C. McIntosh, G. Hamarneh, Greg Mori
{"title":"Human Limb Delineation and Joint Position Recovery Using Localized Boundary Models","authors":"C. McIntosh, G. Hamarneh, Greg Mori","doi":"10.1109/WMVC.2007.18","DOIUrl":"https://doi.org/10.1109/WMVC.2007.18","url":null,"abstract":"We outline the development of a self-initializing kinematic tracker that automatically discovers its part appearance models from a video sequence. Through its unique combination of an existing global joint estimation technique and a robust physical deformation based local search method, the tracker is demonstrated as a novel approach to recovering 2D human joint locations and limb outlines from video sequences. Appearance models are discovered and employed through a novel use of the deformable organisms framework which we have extended to the temporal domain. Quantitative and qualitative results for a set of five test videos are provided. The results demonstrate an overall improvement in tracking performance and that the method is relatively insensitive to initialization, an important consideration in gradient descent-style search algorithms.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129038141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 19
Real Time Viterbi Optimization of Hidden Markov Models for Multi Target Tracking 多目标跟踪隐马尔可夫模型的实时Viterbi优化
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-23 DOI: 10.1109/WMVC.2007.33
Hakan Ardo, K. Astrom, R. Berthilsson
{"title":"Real Time Viterbi Optimization of Hidden Markov Models for Multi Target Tracking","authors":"Hakan Ardo, K. Astrom, R. Berthilsson","doi":"10.1109/WMVC.2007.33","DOIUrl":"https://doi.org/10.1109/WMVC.2007.33","url":null,"abstract":"In this paper the problem of tracking multiple objects in im- age sequences is studied. A Hidden Markov Model describ- ing the movements of multiple objects is presented. Previ- ously similar models have been used, but in real time sys- tem the standard dynamic programming Viterbi algorithm is typically not used to find the global optimum state se- quence, as it requires that all past and future observations are available. In this paper we present an extension to the Viterbi algorithm that allows it to operate on infinite time sequences and produce the optimum with only a finite de- lay. This makes it possible to use the Viterbi algorithm in real time applications. Also, to handle the large state spaces of these models another extension is proposed. The global optimum is found by iteratively running an approximative algorithm with higher and higher precision. The algorithm can determine when the global optimum is found by main- taining an upper bound on all state sequences not evalu- ated. For real time performance some approximations are needed and two such approximations are suggested. The theory has been tested on three real data experiments, all with promising results.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131178440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 25
Gait Analysis For Human Identification Through Manifold Learning and HMM 基于流形学习和HMM的步态识别
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-23 DOI: 10.1109/ISCAS.2007.378088
Ming-Hsu Cheng, Meng-Fen Ho, Chung-Lin Huang
{"title":"Gait Analysis For Human Identification Through Manifold Learning and HMM","authors":"Ming-Hsu Cheng, Meng-Fen Ho, Chung-Lin Huang","doi":"10.1109/ISCAS.2007.378088","DOIUrl":"https://doi.org/10.1109/ISCAS.2007.378088","url":null,"abstract":"With the increasing demands of visual surveillance systems, human identification at a distance has gained more interest. Gait is often used as an unobtrusive biometric offering the possibility to identify individuals at a distance without any interaction or co-operation with the subject. This paper presents a novel effectively method for automatic viewpoint and person identification by using only the sequence of gait silhouette. The gait silhouettes are nonlinearly transformed into low dimensional embedding and the dynamics in time-series images are modeled by HMM in the corresponding embedding space. The experimental results demonstrate that the proposed algorithm is an encouraging progress for automatic human identification.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131251340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 76
A Multiscale Parametric Background Model for Stationary Foreground Object Detection 静止前景目标检测的多尺度参数背景模型
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-23 DOI: 10.1109/WMVC.2007.1
Steven Cheng, Xingzhi Luo, S. Bhandarkar
{"title":"A Multiscale Parametric Background Model for Stationary Foreground Object Detection","authors":"Steven Cheng, Xingzhi Luo, S. Bhandarkar","doi":"10.1109/WMVC.2007.1","DOIUrl":"https://doi.org/10.1109/WMVC.2007.1","url":null,"abstract":"Detection of stationary foreground objects within a dynamic scene is one of the goals of a video surveillance system. A parametric background maintenance and updating scheme, based on a multiple Gaussian mixture model that operates on multiple time scales, is proposed. Each color cluster in the proposed model is assigned a weight which measures the time duration and temporal recurrence frequency of the cluster. Sudden illumination changes are handled by using an adaptive histogram template whereas gradual illumination changes are automatically resolved with the adaptive background model. Stationary foreground objects are detected by maintaining their temporal history in the dynamic scene at multiple time scales. Experimental results show that the proposed scheme performs well in three distinct real-world settings.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116137999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 35
Non-orthogonal Binary Expansion of Gabor Filters with Applications in Object Tracking Gabor滤波器的非正交二进制展开及其在目标跟踪中的应用
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-23 DOI: 10.1109/WMVC.2007.30
Feng Tang, Hai Tao
{"title":"Non-orthogonal Binary Expansion of Gabor Filters with Applications in Object Tracking","authors":"Feng Tang, Hai Tao","doi":"10.1109/WMVC.2007.30","DOIUrl":"https://doi.org/10.1109/WMVC.2007.30","url":null,"abstract":"Gabor filter response is widely used in many computer vision applications for its effectiveness in representing local image details. The major drawback of Gabor features is the high computation cost involved in the convolution between the image and the filter bank. This paper presents a method to approximate the Gabor filters as a linear combination of Haar-like features. These features are selected from a large redundant feature pool using a generative feature selection scheme - optimized orthogonal matching pursuit (OOMP). Major advantage of this representation is that the convolution between the image and the approximated Gabor filters can be computed very efficiently using integral image trick. We applied the proposed method to object tracking, promising results are demonstrated.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120939736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Body Part Detection for Human Pose Estimation and Tracking 人体姿态估计与跟踪的身体部位检测
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-23 DOI: 10.1109/WMVC.2007.10
M. Lee, R. Nevatia
{"title":"Body Part Detection for Human Pose Estimation and Tracking","authors":"M. Lee, R. Nevatia","doi":"10.1109/WMVC.2007.10","DOIUrl":"https://doi.org/10.1109/WMVC.2007.10","url":null,"abstract":"Accurate 3-D human body pose tracking from a monocular video stream is important for a number of applications. We describe a novel hierarchical approach for tracking human pose that uses edge-based features during the coarse stage and later other features for global optimization. At first, humans are detected by motion and tracked by fitting an ellipse in the image. Then, body components are found using edge features and used to estimate the 2D positions of the body joints accurately. This helps to bootstrap the estimation of 3D pose using a sampling-based search method in the last stage. We present experiment results with sequences of different realistic scenes to illustrate the performance of the method.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122385799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 77
Fusion of Multiple Camera Views for Kernel-Based 3D Tracking 基于核的三维跟踪多摄像机视图融合
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-23 DOI: 10.1109/WMVC.2007.15
A. Tyagi, G. Potamianos, James W. Davis, Stephen M. Chu
{"title":"Fusion of Multiple Camera Views for Kernel-Based 3D Tracking","authors":"A. Tyagi, G. Potamianos, James W. Davis, Stephen M. Chu","doi":"10.1109/WMVC.2007.15","DOIUrl":"https://doi.org/10.1109/WMVC.2007.15","url":null,"abstract":"We present a computer vision system to robustly track an object in 3D by combining evidence from multiple calibrated cameras. Its novelty lies in the proposed unified approach to 3D kernel based tracking, that amounts to fusing the appearance features from all available camera sensors, as opposed to tracking the object appearance in the individual 2D views and fusing the results. The elegance of the method resides in its inherent ability to handle problems encountered by various 2D trackers, including scale selection, occlusion, view-dependence, and correspondence across different views. We apply the method on the CHIL project database for tracking the presenter¿s head during lectures inside smart rooms equipped with four calibrated cameras. As compared to traditional 2D based mean shift tracking approaches, the proposed algorithm results in 35% relative reduction in overall 3D tracking error and a 70% reduction in the number of tracker re-initializations.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"137 18-19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116560019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 29
Activity Identification Utilizing Data Mining Techniques 利用数据挖掘技术进行活动识别
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-23 DOI: 10.1109/WMVC.2007.4
J. Lee, W. Hoff
{"title":"Activity Identification Utilizing Data Mining Techniques","authors":"J. Lee, W. Hoff","doi":"10.1109/WMVC.2007.4","DOIUrl":"https://doi.org/10.1109/WMVC.2007.4","url":null,"abstract":"We propose a novel method that, given an unknown moving object trajectory, determines which known activity type the trajectory would belong to. The proposed method utilizes various data mining techniques, including clustering, classification, and Markov model. We collect trajectories of moving objects of known activity types and build one Markov model for each activity type. Given an unknown trajectory, we compute the likelihood of this trajectory belonging to each activity type using the Markov model and the trajectory is determined to belong to the activity type that results in the highest likelihood. We use only location information of moving objects. We do not use any other information such as color, size, or shape of objects, or contextual information. We demonstrate the effectiveness of this method using trajectories of students playing two sports activities Ultimate Frisbee and volleyball. We show that the accuracy of this method is as high as 94%.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127695035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
Recovering the Basic Structure of Human Activities from a Video-Based Symbol String 从基于视频的符号串中恢复人类活动的基本结构
2007 IEEE Workshop on Motion and Video Computing (WMVC'07) Pub Date : 2007-02-01 DOI: 10.1109/WMVC.2007.34
Kris M. Kitani, Yoichi Sato, A. Sugimoto
{"title":"Recovering the Basic Structure of Human Activities from a Video-Based Symbol String","authors":"Kris M. Kitani, Yoichi Sato, A. Sugimoto","doi":"10.1109/WMVC.2007.34","DOIUrl":"https://doi.org/10.1109/WMVC.2007.34","url":null,"abstract":"In recent years stochastic context-free grammars have been shown to be effective in modeling human activities because of the hierarchical structures they represent. However, most of the research in this area has yet to address the issue of learning the activity grammars from a noisy input source, namely, video. In this paper, we present a framework for identifying noise and recovering the basic activity grammar from a noisy symbol string produced by video. We identify the noise symbols by finding the set of non-noise symbols that optimally compresses the training data, where the optimality of compression is measured using an MDL criterion. We show the robustness of our system to noise and its effectiveness in learning the basic structure of human activity, through an experiment with real video from a local convenience store.","PeriodicalId":177842,"journal":{"name":"2007 IEEE Workshop on Motion and Video Computing (WMVC'07)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128565254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 25
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