Abstract:

To solve the problem that multiple instance learing can cause error accumulation in object tracking algorithm, a new object tracking algorithm was proposed based on co-training and online multiple instance learning. This algorithm uses co-training to overcome errors accumulation caused by self-training and improve the robustness of tracking performance based on online multiple instance learning. The center error between tracking results and ideal object location was used as evaluation criteria. The semi-supervised learning tracking algorithm and traditional multiple instance learning tracking algorithm were simulated on the video frames which come from standard video library. The tracking results show that the algorithm performance is more superior, and the center error curves further demonstrate the experimental results. Experimental results show that the proposed approach can track the target well in the complex conditions, and it has a better robustness.

Key words: multiple instance learning, co-training, object tracking, online learning, object detection