Combining Colour and Orientation for Adaptive Particle Filter-based Tracking

We propose an accurate tracking algorithm based on a multi-feature statistical model. The model combines in a single particle filter colour and gradient-based orientation information. A reliability measure derived from the particle distribution is used to adaptively weigh the contribution of the two features. Furthermore, information from the tracker is used to set the dimension of the filters for the computation of the gradient, effectively solving the scale selection problem. Experiments over a set of real-world sequences show that the adaptive use of colour and orientation information improves over either feature taken separately, both in terms of tracking accuracy and of reduction of lost tracks. Also, the automatic scale selection for the derivative filters results in increased robustness.