Robust visual tracking combining global and local appearance models

In this paper, we present a robust visual tracking method combining global and local appearance models. We model the object to be tracked with a RGB color histogram and multiple histograms of oriented gradients (HOG). Modeling object using only the former, a global appearance model, is widely used in visual tracking. However, it suffers many challenges such as illumination changes and pose changes and so on. In order to overcome this problem, we also model the object with multiple block based HOG histograms. The HOG histogram is a local appearance model and can effectively represent the shape information of the object which also gain increasing interests in computer vision especially in pedestrian detection. These two appearance models are complementary and used in the particle filter tracking framework. We test the performance of the proposed method on several challenging sequences, which verifies that our method outperforms the standard particle filter and achieves significant improvement.