Object tracking based on online partial instance learning with multiple local strong classifiers

In this paper, we propose a new appearance model based on Partial Instance Learning (PIL) with multiple local strong classifiers. The key idea of PIL is that image examples are divided into several partial image examples (or local-images), each of which is then independently trained with a local strong classifier. Finally, a tracker is updated for the optimal solution in the sense that the joint probability of partial image examples for each input image example becomes the largest. The proposed PIL method can be considered a risk diversification strategy for unpredictable partial occlusions or appearance changes of an object. Also, it can be regarded as a divide-and-conquer method of Online Boosting (OB), so that PIL only requires approximately 20% of computations compared with other OB methods in terms of iterations taken for learning process. Experiment results show that the proposed PIL-based object tracking method achieves better performance in tracking accuracy and much faster processing speed than other compared real-time based ones.