Subgraph decomposition for multi-target tracking

Tracking multiple targets in a video, based on a finite set of detection hypotheses, is a persistent problem in computer vision. A common strategy for tracking is to first select hypotheses spatially and then to link these over time while maintaining disjoint path constraints [14, 15, 24]. In crowde...

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Bibliographic Details
Published in2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5033 - 5041
Main Authors Siyu Tang, Andres, Bjoern, Andriluka, Mykhaylo, Schiele, Bernt
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Analytical models
Image edge detection
Joining processes
Linear programming
Optimization
Random variables
Target tracking
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Summary:Tracking multiple targets in a video, based on a finite set of detection hypotheses, is a persistent problem in computer vision. A common strategy for tracking is to first select hypotheses spatially and then to link these over time while maintaining disjoint path constraints [14, 15, 24]. In crowded scenes multiple hypotheses will often be similar to each other making selection of optimal links an unnecessary hard optimization problem due to the sequential treatment of space and time. Embracing this observation, we propose to link and cluster plausible detections jointly across space and time. Specifically, we state multi-target tracking as a Minimum Cost Subgraph Multicut Problem. Evidence about pairs of detection hypotheses is incorporated whether the detections are in the same frame, neighboring frames or distant frames. This facilitates long-range re-identification and within-frame clustering. Results for published benchmark sequences demonstrate the superiority of this approach.
ISSN:1063-6919
DOI:10.1109/CVPR.2015.7299138