Fast pedestrian detection using deformable part model and pyramid layer location

The majority of pedestrian detection approaches use multiscale detection and the sliding window search scheme with high computing complexity. We present a fast pedestrian detection method using the deformable part model and pyramid layer location (PLL). First, the object proposal method is used rather than the traditional sliding window to obtain pedestrian proposal regions. Then, a PLL method is proposed to select the optimal root level in the feature pyramid for each candidate window. On this basis, a single-point calculation scheme is designed to calculate the scores of candidate windows efficiently. Finally, pedestrians can be located from the images. The Institut national de recherche en informatique et en automatique dataset for human detection is used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method can reduce the number of feature maps and windows requiring calculation in the detection process. Consequently, the computing cost is significantly reduced, with fewer false positives.