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Optimal Multi-View Fusion of Object Locations 目标位置的最优多视图融合
2008 IEEE Workshop on Motion and video Computing Pub Date : 2008-01-08 DOI: 10.1109/WMVC.2008.4544048
A. Sankaranarayanan, Ramalingam Chellappa
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引用次数: 22
Event-Based Tracking Evaluation Metric 基于事件的跟踪评估度量
2008 IEEE Workshop on Motion and video Computing Pub Date : 2008-01-08 DOI: 10.1109/WMVC.2008.4544059
D. Roth, E. Koller-Meier, D. Rowe, T. Moeslund, L. Van Gool
{"title":"Event-Based Tracking Evaluation Metric","authors":"D. Roth, E. Koller-Meier, D. Rowe, T. Moeslund, L. Van Gool","doi":"10.1109/WMVC.2008.4544059","DOIUrl":"https://doi.org/10.1109/WMVC.2008.4544059","url":null,"abstract":"This paper describes a novel tracking performance evaluation metric based on the successful detection of events, rather than low-level image processing criteria. A general event metric is defined to measure whether the agents and actions in the scene given by the ground truth were correctly tracked by comparing two event lists using dynamic programming. This metric is suitable to evaluate and compare different tracking approaches where the underlying algorithm may be completely different. Furthermore, we introduce an automatic extraction of those semantically high level events from different types of low level tracking data and human annotated ground truth. A case study with two different trackers on public datasets shows the effectiveness of this evaluation scheme.","PeriodicalId":150666,"journal":{"name":"2008 IEEE Workshop on Motion and video Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126487200","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: 15
Detecting Semantic Group Activities Using Relational Clustering 使用关系聚类检测语义组活动
2008 IEEE Workshop on Motion and video Computing Pub Date : 2008-01-01 DOI: 10.1109/WMVC.2008.4544062
A. Hoogs, S. Bush, G. Brooksby, A. Perera, M. Dausch, N. Krahnstoever
{"title":"Detecting Semantic Group Activities Using Relational Clustering","authors":"A. Hoogs, S. Bush, G. Brooksby, A. Perera, M. Dausch, N. Krahnstoever","doi":"10.1109/WMVC.2008.4544062","DOIUrl":"https://doi.org/10.1109/WMVC.2008.4544062","url":null,"abstract":"Existing approaches to detect modeled activities in video often require the precise specification of the number of actors or roles, or spatial constraints, or other limitations that create difficulties for generic detection of group activities. We develop an approach to detect group behaviors in video, where an arbitrary number of participants are involved. We address scene conditions with non-participating objects, an arbitrary number of instances of the behaviors of interest, and arbitrary locations for those instances. Our approach uses semantic spatio-temporal predicates to define activities, and relational clustering to identify groups of objects for which the relational predicates are mutually true over time. The algorithm handles conditions where object segmentation and tracking are highly unreliable, such as busy scenes with occluders. Results are shown for the group activities of crowd formation and dispersal on low-resolution, far-field video surveillance data.","PeriodicalId":150666,"journal":{"name":"2008 IEEE Workshop on Motion and video Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129928100","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
Location-specific Transition Distributions for Tracking 用于跟踪的特定位置的转换分布
2008 IEEE Workshop on Motion and video Computing Pub Date : 2008-01-01 DOI: 10.1109/WMVC.2008.4544061
Nathan Jacobs, M. Dixon, Robert Pless
{"title":"Location-specific Transition Distributions for Tracking","authors":"Nathan Jacobs, M. Dixon, Robert Pless","doi":"10.1109/WMVC.2008.4544061","DOIUrl":"https://doi.org/10.1109/WMVC.2008.4544061","url":null,"abstract":"Surveillance and tracking systems often observe the same scene over extended time periods. When object motion is constrained by the scene (for instance, cars on roads, or pedestrians on sidewalks), it is advantageous to characterize and use scene-specific and location-specific priors to aid the tracking algorithm. This paper develops and demonstrates a method for creating priors for tracking that are conditioned on the current location of the object in the scene. These priors can be naturally incorporated in a number of tracking algorithms to make tracking more efficient and more accurate. We present a novel method to sample from these priors and show performance improvements (in both efficiency and accuracy) for two different tracking algorithms in two different problem domains.","PeriodicalId":150666,"journal":{"name":"2008 IEEE Workshop on Motion and video Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115452865","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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