An Adaptive Combination of Multiple Features for Robust Tracking in Real Scene

Abstract

Real scene video surveillance always involves low resolutions, lack of illumination or cluttered environments, which leads to insufficiency of discriminative details for the target. In this situation, discrimination based tracking methods could fail. To address this problem, this paper presents an adaptive multi-feature integration method in terms of feature invariance, which can evaluate the stability of features in sequential frames. The adaptive integrated feature (AIF) is consisted of several features with dynamic weights, which describe the degree of invariance of each single feature. An incremental principal component analysis (IPCA) adjusted by the accuracy of tracking results is used to update the adaptive integrated feature, and partially avoids the problem of "updating dilemma'', which is common in most of adaptive updating methods. Experiments on pedestrian tracking demonstrate the proposed approach is effective and shows improved performance compared with several state-of-the-art methods in real surveillance scenes.