Joint global-local information pedestrian detection algorithm for outdoor video surveillance

The pedestrian size is usually small in practical outdoor surveillances. The small-scale pedestrian detection for outdoor surveillances is an important but difficult issue due to the limited information and the background interference. According to human cognition, the global information is important for the pedestrian detection. Therefore, a joint global-local information pedestrian detection algorithm is proposed to fully exploit and utilize the global information. The LBP feature is explicitly extracted from the low-frequency component of original images, which are utilized as the global information to suppress the background interference and enrich the description of pedestrian. Moreover, a structure-LBP is proposed to apply the inherent topology structure of human body to LBP. The structure-LBP feature extracted from original images can achieve a more discriminative description of pedestrians compared with the original LBP. The experimental results demonstrate that the proposed algorithm can improve the overall recognition performance for the small-scale pedestrians.