Abstract
Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, generic particle filter (GPF) is based on Monte Carlo approach and sampling is a problematic issue. This paper introduces a parzen particle filter (PPF) which uses a general kernel approach to better approximate the posterior distribution rather than Dirac delta kernel in GPF. Furthermore, we adopt multiple cues and combine texture described by directional energy from multi-scale, multi-orientation steerable filtering with color to characterize our tracking targets. The advantages of tracking with multiple cues compared to individual ones are demonstrated over experiments on artificial and natural sequences.
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Song, L., Zhang, R., Liu, Z., Chen, X. (2007). Object Tracking Based on Parzen Particle Filter Using Multiple Cues. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_23
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DOI: https://doi.org/10.1007/978-3-540-77255-2_23
Publisher Name: Springer, Berlin, Heidelberg
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