Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/588173
Title: Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking
Authors: Longyin Wen;Zhen Lei;Siwei Lyu;Stan Z. Li;Ming-Hsuan Yang
subject: hierarchical|Multi-object tracking|tracklet|undirected affinity hypergraph|dense structures
Year: 2016
Publisher: IEEE
Abstract: Most multi-object tracking algorithms are developed within the tracking-by-detection framework that consider the pairwise appearance similarities between detection responses or tracklets within a limited temporal window, and thus less effective in handling long-term occlusions or distinguishing spatially close targets with similar appearance in crowded scenes. In this work, we propose an algorithm that formulates the multi-object tracking task as one to exploit hierarchical dense structures on an undirected hypergraph constructed based on tracklet affinity. The dense structures indicate a group of vertices that are inter-connected with a set of hyperedges with high affinity values. The appearance and motion similarities among multiple tracklets across the spatio-temporal domain are considered globally by exploiting high-order similarities rather than pairwise ones, thereby facilitating distinguish spatially close targets with similar appearance. In addition, the hierarchical design of the optimization process helps the proposed tracking algorithm handle long-term occlusions robustly. Extensive experiments on various challenging datasets of both multi-pedestrian and multi-face tracking tasks, demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Description: 
URI: http://localhost/handle/Hannan/172533
http://localhost/handle/Hannan/588173
ISSN: 0162-8828
volume: 38
issue: 10
Appears in Collections:2016

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Title: Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking
Authors: Longyin Wen;Zhen Lei;Siwei Lyu;Stan Z. Li;Ming-Hsuan Yang
subject: hierarchical|Multi-object tracking|tracklet|undirected affinity hypergraph|dense structures
Year: 2016
Publisher: IEEE
Abstract: Most multi-object tracking algorithms are developed within the tracking-by-detection framework that consider the pairwise appearance similarities between detection responses or tracklets within a limited temporal window, and thus less effective in handling long-term occlusions or distinguishing spatially close targets with similar appearance in crowded scenes. In this work, we propose an algorithm that formulates the multi-object tracking task as one to exploit hierarchical dense structures on an undirected hypergraph constructed based on tracklet affinity. The dense structures indicate a group of vertices that are inter-connected with a set of hyperedges with high affinity values. The appearance and motion similarities among multiple tracklets across the spatio-temporal domain are considered globally by exploiting high-order similarities rather than pairwise ones, thereby facilitating distinguish spatially close targets with similar appearance. In addition, the hierarchical design of the optimization process helps the proposed tracking algorithm handle long-term occlusions robustly. Extensive experiments on various challenging datasets of both multi-pedestrian and multi-face tracking tasks, demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Description: 
URI: http://localhost/handle/Hannan/172533
http://localhost/handle/Hannan/588173
ISSN: 0162-8828
volume: 38
issue: 10
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360186.pdf1.94 MBAdobe PDFThumbnail
Preview File
Title: Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking
Authors: Longyin Wen;Zhen Lei;Siwei Lyu;Stan Z. Li;Ming-Hsuan Yang
subject: hierarchical|Multi-object tracking|tracklet|undirected affinity hypergraph|dense structures
Year: 2016
Publisher: IEEE
Abstract: Most multi-object tracking algorithms are developed within the tracking-by-detection framework that consider the pairwise appearance similarities between detection responses or tracklets within a limited temporal window, and thus less effective in handling long-term occlusions or distinguishing spatially close targets with similar appearance in crowded scenes. In this work, we propose an algorithm that formulates the multi-object tracking task as one to exploit hierarchical dense structures on an undirected hypergraph constructed based on tracklet affinity. The dense structures indicate a group of vertices that are inter-connected with a set of hyperedges with high affinity values. The appearance and motion similarities among multiple tracklets across the spatio-temporal domain are considered globally by exploiting high-order similarities rather than pairwise ones, thereby facilitating distinguish spatially close targets with similar appearance. In addition, the hierarchical design of the optimization process helps the proposed tracking algorithm handle long-term occlusions robustly. Extensive experiments on various challenging datasets of both multi-pedestrian and multi-face tracking tasks, demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Description: 
URI: http://localhost/handle/Hannan/172533
http://localhost/handle/Hannan/588173
ISSN: 0162-8828
volume: 38
issue: 10
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360186.pdf1.94 MBAdobe PDFThumbnail
Preview File