Meta-Tracking for Video Scene Understanding

Abstract : This paper presents a novel method to extract dominant motion patterns (MPs) and the main entry/exit areas from a surveillance video. The method first computes motion histograms for each pixel and then converts it into orientation distribution functions (ODFs). Given these ODFs, a novel particle meta-tracking procedure is launched which produces meta-tracks, i.e. particle trajectories. As opposed to conventional tracking which focuses on individual moving objects, meta-tracking uses particles to follow the dominant flow of the traffic. In a last step, a novel method is used to simultaneously identify the main entry/exit areas and recover the predominant MPs. The meta-tracking procedure is a unique way to connect low-level motion features to long-range MPs. This kind of tracking is inspired by brain fiber tractography which has long been used to find dominant connections in the brain. Our method is fast, simple to implement, and works both on sparse and extremely crowded scenes. It also works on highly structured scenes (highways, traffic-light corners, etc.) as well as on chaotic scenes containing moving objects crossing each other and following different directions. As will be shown, our method locates short-range and long-range MPs of arbitrary shape with high accuracy.
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Submitted on : Thursday, October 10, 2013 - 11:07:14 AM
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Pierre-Marc Jodoin, Yannick Benezeth, Yi Wang. Meta-Tracking for Video Scene Understanding. IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Aug 2013, Poland. ⟨hal-00857916⟩

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