Thermal infrared single-pedestrian tracking for advanced driver assistance system

Tracking algorithms with low computational complexity and reliable performance are important in developing advanced driver assistance systems (DAS). This paper proposes a method of single-pedestrian tracking using thermal infrared cameras to meet the needs of DAS operating in nighttime and low-visibility conditions. The proposed algorithm uses the background-aware correlation filter (BACF) as the basic tracking framework. In order to address the problem that directly introducing the convolutional features leads to tracking performance degradation in the BACF framework, this paper proposes a fusion scheme to integrate handcrafted and convolutional features to make full use of the advantages of both the features. The proposed scheme combines response maps from convolutional and handcrafted features through fusion coefficients to improve the performance of the trackers based on the single features. In order to calculate fusion coefficients, a novel approach of searching the main peak and interference peaks of a response map is proposed by using local binary pattern values of the response map to locate all local maximum points. Experimental results show that the proposed algorithm outperforms the existing 9 competing tracking algorithms and can be used in vehicle platforms as a module of DAS to improve the safe level of driving in nighttime.