Visual tracking with probabilistic data association filter based on the circular hough transform

This paper proposes a robust visual tracking framework to track circle-like objects in cluttered environment. Instead of using the resulting positions after circle detection and tracking in the image domain, we directly perform the visual tracking task in the parameter space which describes the measurement features. The visual tracking technique is combined with targets generation closely. We utilize the probabilistic data association filter (PDAF) to filter the detected measurements with noise and disturbance. The likelihood ratio through the Hough transform is employed to modify the evaluation of the association probability and make the estimate more reliable. Furthermore, the joint probabilistic data association filter (JPDAF) is used to deal with the multiple circle-like objects tracking. The likelihood variation of each target is introduced with JPDAF as a basis of the predictions for different targets. The overall performance has been verified in several challenging experiments