frames. A particle filter (PF) and a Gaussian sum particle filter (GSPF) are developed based upon multiple information cues, namely colour and texture, which are described with highly nonlinear models. The algorithms rely on likelihood factorisation as a product of the likelihoods of the cues. We demonstrate the advantages of tracking with multiple independent complementary cues compared to tracking with individual cues. The advantages are increased robustness and improved accuracy. The performance of the two filters is investigated and validated over both synthetic and natural video sequences.
|Translated title of the contribution||Particle filtering with multiple cues for object tracking in video sequences|
|Title of host publication||IS & T/SPIE 17th Annual Symposium Image and Video Communications Processing 2005, San Jose, CA, USA|
|Publisher||Society of Photo-Optical Instrumentation Engineers (SPIE)|
|Pages||430 - 441|
|Number of pages||12|
|Publication status||Published - 18 Jan 2005|