Robust particle filtering for object tracking

This paper addresses the filtering problem when no assumption about linearity or gaussianity is made an the involved density functions. This approach, widely known as particle filtering, has been explored by several previous algorithms, including Condensation. Although it represented a new paradigm and promising results have been achieved, it has several unpleasant behaviours. We highlight these misbehaviours and propose an algorithm which deals with them. A test-bed, which allows proof-testing of new approaches, has been developed. The proposal has been successfully tested using both synthetic and real sequences.