ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream最新文献

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Enhanced human behavior recognition using HMM and evaluative rectification 利用HMM和评价校正增强人类行为识别
N. Doulamis, A. Voulodimos, D. Kosmopoulos, T. Varvarigou
{"title":"Enhanced human behavior recognition using HMM and evaluative rectification","authors":"N. Doulamis, A. Voulodimos, D. Kosmopoulos, T. Varvarigou","doi":"10.1145/1877868.1877880","DOIUrl":"https://doi.org/10.1145/1877868.1877880","url":null,"abstract":"Human behavior recognition and real world environments monitoring constitute challenging research problems rapidly gaining momentum over the last years. Methods for time series classification like the Hidden Markov Models have been employed in the past for similar tasks, however in many challenging cases they fail, since some behaviors are much more difficult to model than others. This happens particularly in cases that there is scarcity of labelled data.\u0000 In this paper we introduce a novel re-adjustment framework of behavior recognition and classification by allowing the user incorporation in the learning process.\u0000 The proposed Evaluative Rectification approach aims at dynamically correcting erroneous classification results to enhance the behavior modeling and therefore the overall classification rates. We evaluate the performance of the examined approach in a challenging real-life industrial environment of an automobile manufacturer. Our experiments indicate a significant outperformance of the proposed Evaluative Rectification scheme compared with traditional classification frameworks, such as Hidden Markov Models.","PeriodicalId":360789,"journal":{"name":"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128459561","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
Real time illumination invariant motion change detection 实时照明不变运动变化检测
Konstantinos Avgerinakis, A. Briassouli, Y. Kompatsiaris
{"title":"Real time illumination invariant motion change detection","authors":"Konstantinos Avgerinakis, A. Briassouli, Y. Kompatsiaris","doi":"10.1145/1877868.1877887","DOIUrl":"https://doi.org/10.1145/1877868.1877887","url":null,"abstract":"An original approach for real time detection of changes in motion is presented, which can lead to the detection and recognition of events. Current video change detection focuses on shot changes which depend on appearance, not motion. Changes in motion are detected in pixels that are found to be active via the kurtosis. Statistical modeling of the motion data shows that the Laplace distribution provides the most accurate fit. The Laplace model of the motion is used in a sequential change detection test, which detects the changes in real time. False alarm detection determined whether a detected change is indeed induced by motion or by varying scene illumination. This leads to precise detection of changes in motion for many videos, where shot change detection if shown to fail. Experiments show that the proposed method finds meaningful changes in real time, even under conditions of varying scene illumination.","PeriodicalId":360789,"journal":{"name":"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122349686","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: 3
Dense spatio-temporal features for non-parametric anomaly detection and localization 用于非参数异常检测和定位的密集时空特征
Lorenzo Seidenari, M. Bertini, A. Bimbo
{"title":"Dense spatio-temporal features for non-parametric anomaly detection and localization","authors":"Lorenzo Seidenari, M. Bertini, A. Bimbo","doi":"10.1145/1877868.1877877","DOIUrl":"https://doi.org/10.1145/1877868.1877877","url":null,"abstract":"In this paper we propose dense spatio-temporal features to capture scene dynamic statistics together with appearance, in video surveillance applications. These features are exploited in a real-time anomaly detection system. Anomaly detection is performed using a non-parametric modelling, evaluating directly local descriptor statistics, and an unsupervised or semi-supervised approach. A method to update scene statistics, to cope with scene changes that typically happen in real world settings, is also provided. The proposed method is tested on publicly available datasets and compared to other state-of-the-art approaches.","PeriodicalId":360789,"journal":{"name":"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130757488","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
Human action recognition with MPEG-7 descriptors and architectures 使用MPEG-7描述符和体系结构的人类动作识别
Z. Moghaddam, M. Piccardi
{"title":"Human action recognition with MPEG-7 descriptors and architectures","authors":"Z. Moghaddam, M. Piccardi","doi":"10.1145/1877868.1877885","DOIUrl":"https://doi.org/10.1145/1877868.1877885","url":null,"abstract":"Modern video surveillance requires addressing high-level concepts such as humans' actions and activities. In addition, surveillance applications need to be portable over a variety of platforms, from servers to mobile devices. In this paper, we explore the potential of the MPEG-7 standard to provide interfaces, descriptors, and architectures for human action recognition from surveillance cameras. Two novel MPEG-7 descriptors, symbolic and feature-based, are presented alongside two different architectures, server-intensive and client-intensive. The descriptors and architectures are evaluated in the paper by way of a scenario analysis.","PeriodicalId":360789,"journal":{"name":"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127574386","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: 4
Joint multitarget object tracking and interaction analysis by a probabilistic bio-inspired model 基于概率仿生模型的联合多目标跟踪与交互分析
Francesco Monti, S. Maludrottu, C. Regazzoni
{"title":"Joint multitarget object tracking and interaction analysis by a probabilistic bio-inspired model","authors":"Francesco Monti, S. Maludrottu, C. Regazzoni","doi":"10.1145/1877868.1877874","DOIUrl":"https://doi.org/10.1145/1877868.1877874","url":null,"abstract":"In this paper a joint human tracking and human-to-human interaction recognition system is proposed. While usually these two functions are performed separately, it will be shown that it is possible to improve the tracking performances if these functions are done jointly. For this purpose, a Bayesian tracking algorithm is coupled with a bio-inspired interaction analysis framework. The motion patterns of moving entities provided by the tracker are analyzed in order to recognize the current situation; causal relationships between interacting individuals in the environment are formulated in terms of probabilistic distributions that are used to cue the tracker in closed loop. The effectiveness of the proposed approach is demonstrated for a variety of image sequences.","PeriodicalId":360789,"journal":{"name":"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114779654","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: 0
Event-based unobtrusive authentication using multi-view image sequences 使用多视图图像序列的基于事件的非突兀身份验证
A. Drosou, K. Moustakas, D. Tzovaras
{"title":"Event-based unobtrusive authentication using multi-view image sequences","authors":"A. Drosou, K. Moustakas, D. Tzovaras","doi":"10.1145/1877868.1877886","DOIUrl":"https://doi.org/10.1145/1877868.1877886","url":null,"abstract":"his paper presents a novel framework for dynamic activity-related user authentication utilizing dynamic and static anthropometric information. The recognition of the performed activity is based on Radon transforms that are applied on spatiotemporal motion templates. User authentication is performed exploiting the behavioural variations between different users. The upper body limb anthropometric information is extracted for each user and an attributed body-related graph structure framework is employed for the detection of static biometric features of substantial discrimination power. Finally, a quality factor based on ergonomic criteria evaluates the recognition capacity of each activity. Experimental validation illustrates that the proposed approach for integrating static anthropometric features and activity-related recognition advances significantly the authentication performance.","PeriodicalId":360789,"journal":{"name":"ACM/IEEE international workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351647","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: 6
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