Robust Particle Filtering for Object Tracking
This paper addresses the filtering problem when no assumption about linearity or gaussianity is made on 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.
KeywordsWhite Additive Gaussian Noise Object Tracking Background Clutter Distribution Run1 Time Sample Likelihood
- 2.Doucet, A.: On sequential simulation-based methods for bayesian filtering. Technical Report TR310, Cambridge University (1998)Google Scholar
- 5.Russell, R., Norvig, P.: Artificial Intelligence, a Modern Approach, 2nd edn., ch. 13–15. Prentice Hall, Englewood Cliffs (2003)Google Scholar
- 6.van der Merwe, R., de Freitas, N., Doucet, A., Wan, E.: The Unscented Particle Filter. Technical Report TR380, Cambridge University (2000)Google Scholar
- 7.Varona, X., Gonzàlez, J., Roca, X., Villanueva, J.J.: iTrack: Image-based Probabilistic Tracking of People. In: 15th ICPR, Barcelona, Spain, vol. 3, pp. 1110–1113 (2000)Google Scholar