Multi-scale color features based on correlation filter for visual tracking

The change of appearance of the target object is one of important issue in visual tracking. It is because some factors such as camera motion, illumination change, motion change, occlusion, and size change are influenced to the object target during tracking. Recently, discriminative correlation filters (DCF) gave good results to handle these problems. Unfortunately, the DCF only works in the single-resolution features maps. In this paper, multi-scale color features are investigated to to solve this limitation. The multi-scale color features are implemented with correlation filter where it works in the continuous domain. In consequence of this reason, the implicit model of the target object is needed. So that, an interpolation is used to solve this problem. The output of this method is selected from the circular convolution response which has maximum value. Extensive experimental results on VOT2015 benchmark dataset which consists of 60 challenging videos show that the multi-scale color features based on correlation filter performs favorably against several state-of-the-art methods.