Adaptive multifeature visual tracking in a probability-hypothesis-density filtering framework

• We give a solution to implementing PHD visual tracker with adaptive multifeature observation model.
• The solution is based on a reliability measure to adaptively adjust the weights for multiple features.
• We compare the performance of the proposed method with original PHD filter based visual trackers.
• The adaptive multifeature PHD visual tracker is superior to original PHD filter based visual trackers.

Probability hypothesis density (PHD) based trackers have enjoyed growing popularity in recent years, particularly in the field of nonlinear non-Gaussian visual tracking scenarios. These visual trackers can be degraded by a variety of factors, including changes of illumination, occlusion, poor image contrast, shape and appearance variation, clutter and other unmodeled changes of tracked objects. In this paper, for enhancing the performance of PHD based trackers, both scale invariant feature and color distribution feature are used as descriptors of targets of interest. To adaptively adjust the weights of each feature in the tracking process, a confidence measure, i.e., a quantitative measure for the spatial uncertainty of each feature is incorporated into the multifeature tracking algorithm. Experimental results show that the proposed multifeature tracker can improve the reliability and robustness of state estimation and the number estimation in tracking a variable number of objects of varying scales even when background region was similar to the object's appearance.