Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/636424
Title: Detection-Free Multiobject Tracking by Reconfigurable Inference With Bundle Representations
Authors: Liang Lin;Yongyi Lu;Chenglong Li;Hui Cheng;Wangmeng Zuo
subject: video processing|Graphical inference|object tracking|spatio-temporal analysis
Year: 2016
Publisher: IEEE
Abstract: This paper presents a conceptually simple but effective approach to track multiobject in videos without requiring elaborate supervision (i.e., training object detectors or templates offline). Our framework performs a bi-layer inference of spatio-temporal grouping to exploit rich appearance and motion information in the observed sequence. First, we generate a robust middle-level video representation based on clustered point tracks, namely video bundles. Each bundle encapsulates a chunk of point tracks satisfying both spatial proximity and temporal coherency. Taking the video bundles as vertices, we build a spatio-temporal graph that incorporates both competitive and compatible relations among vertices. The multiobject tracking can be then phrased as a graph partition problem under the Bayesian framework, and we solve it by developing a reconfigurable belief propagation (BP) algorithm. This algorithm improves the traditional BP method by allowing a converged solution to be reconfigured during optimization, so that the inference can be reactivated once it gets stuck in local minima and thus conduct more reliable results. In the experiments, we demonstrate the superior performances of our approach on the challenging benchmarks compared with other state-of-the-art methods.
URI: http://localhost/handle/Hannan/168223
http://localhost/handle/Hannan/636424
ISSN: 2168-2267
2168-2275
volume: 46
issue: 11
Appears in Collections:2016

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Title: Detection-Free Multiobject Tracking by Reconfigurable Inference With Bundle Representations
Authors: Liang Lin;Yongyi Lu;Chenglong Li;Hui Cheng;Wangmeng Zuo
subject: video processing|Graphical inference|object tracking|spatio-temporal analysis
Year: 2016
Publisher: IEEE
Abstract: This paper presents a conceptually simple but effective approach to track multiobject in videos without requiring elaborate supervision (i.e., training object detectors or templates offline). Our framework performs a bi-layer inference of spatio-temporal grouping to exploit rich appearance and motion information in the observed sequence. First, we generate a robust middle-level video representation based on clustered point tracks, namely video bundles. Each bundle encapsulates a chunk of point tracks satisfying both spatial proximity and temporal coherency. Taking the video bundles as vertices, we build a spatio-temporal graph that incorporates both competitive and compatible relations among vertices. The multiobject tracking can be then phrased as a graph partition problem under the Bayesian framework, and we solve it by developing a reconfigurable belief propagation (BP) algorithm. This algorithm improves the traditional BP method by allowing a converged solution to be reconfigured during optimization, so that the inference can be reactivated once it gets stuck in local minima and thus conduct more reliable results. In the experiments, we demonstrate the superior performances of our approach on the challenging benchmarks compared with other state-of-the-art methods.
URI: http://localhost/handle/Hannan/168223
http://localhost/handle/Hannan/636424
ISSN: 2168-2267
2168-2275
volume: 46
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7283579.pdf2.26 MBAdobe PDFThumbnail
Preview File
Title: Detection-Free Multiobject Tracking by Reconfigurable Inference With Bundle Representations
Authors: Liang Lin;Yongyi Lu;Chenglong Li;Hui Cheng;Wangmeng Zuo
subject: video processing|Graphical inference|object tracking|spatio-temporal analysis
Year: 2016
Publisher: IEEE
Abstract: This paper presents a conceptually simple but effective approach to track multiobject in videos without requiring elaborate supervision (i.e., training object detectors or templates offline). Our framework performs a bi-layer inference of spatio-temporal grouping to exploit rich appearance and motion information in the observed sequence. First, we generate a robust middle-level video representation based on clustered point tracks, namely video bundles. Each bundle encapsulates a chunk of point tracks satisfying both spatial proximity and temporal coherency. Taking the video bundles as vertices, we build a spatio-temporal graph that incorporates both competitive and compatible relations among vertices. The multiobject tracking can be then phrased as a graph partition problem under the Bayesian framework, and we solve it by developing a reconfigurable belief propagation (BP) algorithm. This algorithm improves the traditional BP method by allowing a converged solution to be reconfigured during optimization, so that the inference can be reactivated once it gets stuck in local minima and thus conduct more reliable results. In the experiments, we demonstrate the superior performances of our approach on the challenging benchmarks compared with other state-of-the-art methods.
URI: http://localhost/handle/Hannan/168223
http://localhost/handle/Hannan/636424
ISSN: 2168-2267
2168-2275
volume: 46
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7283579.pdf2.26 MBAdobe PDFThumbnail
Preview File