Abstract
This paper presents a solution to the problem of tracking people within crowded scenes. The aim is to maintain individual object identity through a crowded scene which contains complex interactions and heavy occlusions of people. Our approach uses the strengths of two separate methods; a global object detector and a localised frame by frame tracker. A temporal relationship model of torso detections built during low activity period, is used to further disambiguate during periods of high activity. A single camera with no calibration and no environmental information is used. Results are compared to a standard tracking method and groundtruth. Two video sequences containing interactions, overlaps and occlusions between people are used to demonstrate our approach. The results show that our technique performs better that a standard tracking method and can cope with challenging occlusions and crowd interactions.
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Gilbert, A., Bowden, R. (2007). Multi Person Tracking Within Crowded Scenes. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds) Human Motion – Understanding, Modeling, Capture and Animation. HuMo 2007. Lecture Notes in Computer Science, vol 4814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75703-0_12
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DOI: https://doi.org/10.1007/978-3-540-75703-0_12
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