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Summary: Propagation of Pixel Hypotheses
for Multiple Objects Tracking
Haris Baltzakis and Antonis A. Argyros
Institute of Computer Science, Forth
{xmpalt,argyros}@ics.forth.gr
http://www.ics.forth.gr/cvrl/
Abstract. In this paper we propose a new approach for tracking mul-
tiple 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 ob-
ject tracks by taking into account both the shape of each object and the
uncertainty associated with its track. The proposed tracking approach
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