Improving multi-target tracking via social grouping

We address the problem of multi-person dataassociation-based tracking (DAT) in semi-crowded environments from a single camera. Existing trackletassociation-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 si...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Zhen Qin, Christian R. Shelton
• Keywords: Computer Vision, Cvpr 2012, Global Optimization Problem, Hungarian Algorithm, Nonlinear Global Optimization