Multi-feature tracking via adaptive weights

Abstract

In this work, we present a novel online object tracking algorithm by using multi-feature channels with adaptive weights. Firstly, we exploit intensity, histogram of gradient (HOG) and color naming features to generate a set of confidence maps where the confidence value of each pixel indicates the probability that this pixel belongs to the tracked object. The intensity feature covers the energy information and HOG feature depicts the texture information of the tracked object and its surrounding background respectively. Color naming features aforementioned not only provide high-level features to build a more stable appearance model, but also handle tracking with cluttered coloring background effectively. Secondly, we learn an online model that denotes the close relationship between the center of target and background context, which represents some statistical correlation in consecutive frames. Finally, we exploit the appearance model and online model to generate a confidence map for each feature channel, and then obtain a final confidence map by fusing confidence maps from different channels in an adaptive manner. The optimal location of the tracked object can be determined based on the maximum value in the fused final confidence map. Both qualitative and quantitative evaluations on the recent benchmark dataset demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods, especially for the color sequences.