Thermal infrared pedestrian tracking via fusion of features in driving assistance system of intelligent vehicles

Driving in nighttime or poor illumination is much more dangerous than in daytime or rich illumination conditions so it is important to develop driver assistance systems to support safe driving in nighttime. This paper presents a pedestrian tracking algorithm using a thermal infrared camera. The proposed algorithm first computes response maps with normalized grayscale and Felzenszwalb's histogram of oriented gradient in the framework of correlation filtering. Subsequently, according to response maps, this paper proposes an approach to estimate weight ratios in fusion. The fused response map is ultimately used to complete target detection in the framework of correlation filtering. In order to test the performance of the proposed tracker, extensive comparison and tests have been performed using open-source data sets. By testing through 10 different challenges in image processing and tracking, it is evident that the performance of proposed algorithm is superior to seven representative tracking algorithms with a quite promising real-time performance.