A Pedestrian-Detection Method Based on Heterogeneous Features and Ensemble of Multi-View–Pose Parts

Vision-based pedestrian detection remains a challenging task, so far. The detection performance often suffers from the various appearances of pedestrians, the illumination changes, and the possible partial occlusions. Aiming at resolving these challenges, in this paper, a new linear kernel function is proposed to effectively combine two heterogeneous features, i.e., histogram of oriented gradient and local binary pattern, which enhances the pedestrian description ability to illumination conditions and cluttered background. Then, a novel multi-view-pose part ensemble (MVPPE) detector is proposed, in order to better handle pedestrian variability, views, and partial occlusions. Experimental results in public data sets demonstrate that the proposed feature combination method significantly improves the description capabilities of pedestrian features. Compared with the existing multipart ensemble approaches, the proposed MVPPE detector boosts higher detection accuracy.