Abstract
Kalman filtering has been extensively used in object tracking. However, the tracker performance is severely affected in the presence of multiple objects and cluttered background. The reason is simple. Feature detection produces many outliers and the Kalman filter is not able to discriminate valid data from the clutter. This paper overcome this difficulty and describes a robust algorithm for object tracking denoted as S-PDAF (shape-probabilistic data association filter). Experimental tests show that significant robustness improvement is achieved by the S-PDAF algorithm.
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