Improving Multi-target Tracking via Social Grouping (2012)

by Zhen Qin and Christian R. Shelton

Abstract: We address the problem of multi-person data-association-based tracking (DAT) in semi-crowded environments from a single camera. Existing tracklet-association-based methods using purely visual cues (like appearance and motion information) show impressive results but rely on heavy training, a number of tuned parameters, and sophisticated detectors to cope with visual ambiguities within the video and low-level processing errors. In this work, we consider clustering dynamics to mitigate such ambiguities. This leads to a general optimization framework that adds social grouping behavior (SGB) to any basic affinity model. We formulate this as a nonlinear global optimization problem to maximize the consistency of visual and grouping cues for trajectories in both tracklet-tracklet linking space and tracklet-grouping assignment space. We formulate the Lagrange dual and solve it using a two-stage iterative algorithm, employing the Hungarian algorithm and K-means clustering. We build SGB upon a simple affinity model and show very promising performance on two publicly available real-world datasets with different tracklet extraction methods.

Download Information

Zhen Qin and Christian R. Shelton (2012). "Improving Multi-target Tracking via Social Grouping." IEEE Conference on Computer Vision and Pattern Recognition.

Bibtex citation

@inproceedings{QinShe12,
   author = "Zhen Qin and Christian R. Shelton",
   title = "Improving Multi-target Tracking via Social Grouping",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
   booktitleabbr = "{CVPR}",
   year = 2012,
}

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