Pixel-Pair Features Selection for Vehicle Tracking

This paper proposes a novel tracking algorithm to cope with the appearance variations of vehicle in the natural environments. The algorithm utilizes the discriminative features named pixel-pair features for estimating the similarity between the template image and candidate matching images. Pixel-pair features have been proved to be robust for illumination changes and partial occlusions of the training object. This paper improves the original feature selection algorithm to increase the tracking performance in other appearance changes (such as shape deformation, drifting and view angle change). The new feature selection algorithm incrementally selects the discriminative pixel-pair feature whose matching error between the target and the background is lower than a given threshold. Also the roulette selection method based on the edge values is utilized to increase the possibility to select more informative feature points. The selected features therefore are considered to be robust for shape deformation and view angle changes. Compared with the original feature selection algorithm, our algorithm shows excellent robustness in a variety of videos which include illumination changes, shape deformation, drifting and partial occlusion.