Abstract
In this paper we propose a new approach for tracking multiple objects in image sequences. The proposed approach differs from existing ones in important aspects of the representation of the location and the shape of tracked objects and of the uncertainty associated with them. The location and the speed of each object is modeled as a discrete time, linear dynamical system which is tracked using Kalman filtering. Information about the spatial distribution of the pixels of each tracked object is passed on from frame to frame by propagating a set of pixel hypotheses, uniformly sampled from the original object’s projection to the target frame using the object’s current dynamics, as estimated by the Kalman filter. The density of the propagated pixel hypotheses provides a novel metric that is used to associate image pixels with existing object tracks by taking into account both the shape of each object and the uncertainty associated with its track. The proposed tracking approach has been developed to support face and hand tracking for human-robot interaction. Nevertheless, it is readily applicable to a much broader class of multiple objects tracking problems.
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Baltzakis, H., Argyros, A.A. (2009). Propagation of Pixel Hypotheses for Multiple Objects Tracking. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_13
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DOI: https://doi.org/10.1007/978-3-642-10520-3_13
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