Online semi-supervised multi-person tracking with gaussian process regression

Abstract

Most existing multi-person tracking approaches are affected by lighting condition, pedestrian pose change abruptly, scale changes, realtime processing to name a few, resulting in detection error, drift and other issues. To cope with this challenge, we propose an enhanced multi-person framework by introducing a new observation model, which adaptively updates fully online to avoid the loss of sample diversity and learning in a semi-supervised manner. We fuse prior information for tracking decision, meanwhile extracted knowledge from current frame is used to assist to make tracking decision, which can be viewed as a transfer learning strategy, and both aspects can ameliorate the tendency to drift. The new approach does not need any calibration or batch processing. Experimental results show that the approach yields comparable or better performance in comparison with the state-of-the-arts, which do calibration or batch processing.