{"title":"基于时空轨迹关联的多人跟踪","authors":"Weizhi Nie, Anan Liu, Yuting Su","doi":"10.1109/AVSS.2012.89","DOIUrl":null,"url":null,"abstract":"In the field of video surveillance, multiple object tracking is a challenging problem in the real application. In this paper, we propose a multiple object tracking method by spatiotemporal tracklet association. Firstly, reliable tracklets, the fragments of the entire trajectory of individual object movement, are generated by frame-wise association between object localization results in the neighbor frames. To avoid the negative influence of occlusion on reliable tracklet generation, part-based similarity computation is performed. Secondly, the produced tracklets are associated considering both spatial and temporal constrains to output the entire trajectory for individual person. Especially, we formulate the task of spatiotemporal multiple tracklet matching into a Maximum A Posterior (MAP) problem in the form of Markov Chain with spatiotemporal context constraints. The experiment on PETS 2012 dataset demonstrates the superiority of the proposed method.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Multiple Person Tracking by Spatiotemporal Tracklet Association\",\"authors\":\"Weizhi Nie, Anan Liu, Yuting Su\",\"doi\":\"10.1109/AVSS.2012.89\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the field of video surveillance, multiple object tracking is a challenging problem in the real application. In this paper, we propose a multiple object tracking method by spatiotemporal tracklet association. Firstly, reliable tracklets, the fragments of the entire trajectory of individual object movement, are generated by frame-wise association between object localization results in the neighbor frames. To avoid the negative influence of occlusion on reliable tracklet generation, part-based similarity computation is performed. Secondly, the produced tracklets are associated considering both spatial and temporal constrains to output the entire trajectory for individual person. Especially, we formulate the task of spatiotemporal multiple tracklet matching into a Maximum A Posterior (MAP) problem in the form of Markov Chain with spatiotemporal context constraints. The experiment on PETS 2012 dataset demonstrates the superiority of the proposed method.\",\"PeriodicalId\":275325,\"journal\":{\"name\":\"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AVSS.2012.89\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AVSS.2012.89","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiple Person Tracking by Spatiotemporal Tracklet Association
In the field of video surveillance, multiple object tracking is a challenging problem in the real application. In this paper, we propose a multiple object tracking method by spatiotemporal tracklet association. Firstly, reliable tracklets, the fragments of the entire trajectory of individual object movement, are generated by frame-wise association between object localization results in the neighbor frames. To avoid the negative influence of occlusion on reliable tracklet generation, part-based similarity computation is performed. Secondly, the produced tracklets are associated considering both spatial and temporal constrains to output the entire trajectory for individual person. Especially, we formulate the task of spatiotemporal multiple tracklet matching into a Maximum A Posterior (MAP) problem in the form of Markov Chain with spatiotemporal context constraints. The experiment on PETS 2012 dataset demonstrates the superiority of the proposed method.