For multi-object tracking using a particle filter, a tracking method in which Cross entropy is incorporated into a likelihood function is proposed, with the aim of improving the tracking speed. Baseline methods have utilized Bhattacharyya distance, KL divergence, and so on, in the likelihood function. However, these methods require unnegligible computational cost in calculation of color histograms for each sample, drawn at each frame. In contrast, in the Cross entropy method, likelihood calculations can be performed without generating sample histograms, which is expected to speed-up the tracking speed. Moreover, incorporating the background information into a tracking algorithm is a possible solution for performance improvement. Background information can be utilized together with cross entropy without increasing the computational cost. Therefore, fast and robust tracking algorithm for occlusion problem can be generated by incorporating background information with cross entropy. The proposed method was experimentally compared with a baseline method using the Bhattacharyya distance. The effectiveness of the proposed method and the effect of the number of sample were examined.
|Number of pages||9|
|Journal||Journal of the Institute of Image Electronics Engineers of Japan|
|Publication status||Published - 1 Jan 2010|
- background histogram
- Cross Entropy
- multi-object tracking
- particle filter