Multi-person Tracking Based on Body Parts and Online Random Ferns Learning of Thermal Images

This paper presents a novel algorithm for tracking multiple persons with thermal imaging. The algorithm uses online random ferns (RF) learning to update the model of the person and particle filters to approximate the person's location. To estimate the observational likelihood for particle weighting, we perform online training for the initial ferns using boosted random ferns (BRF) in the first frame in regions where persons are detected. Then, RF for the tracker model is re-trained based on the observed distribution of selected ferns in consecutive frames. To design a robust tracking model impervious to occlusion, we divide person regions into 4 x 4 sub-blocks and then train the RF using concatenated feature vectors from 16 sub-blocks. In addition, we propose an occlusion-check algorithm to distinguish normal object-tracking from long and short-term occlusion. The proposed algorithm is compared with similar existing algorithms to show that its tracking performance is superior to those of other classifiers and tracking methods.