Scan window based pedestrian recognition methods improvement by search space and scale reduction

Most of computation time when dealing with a pedestrian detector is spent in the feature computation and then in the multi-scale classification. This second step consists of applying scanning windows at multiple scales. Depending on the number of scales and on the image dimension, this step is slow because a large number of windows is generated. An efficient pruning algorithm able to remove most of the scan windows brings a significant contribution on the overall execution time. We propose a scan window pruning algorithm based on a combination of several filters: (1) remove windows based on the relation between the dimension and the position in the scene; (2) remove uniform regions from the image such as the sky or the road; (3) remove regions with high density of horizontal edges such as vegetation parts; (4) keep local maxima windows having a high density of connected vertical edges. The combination of these four filters eliminates more than 90% of the scanning windows in a given image and maintains the windows that are overlapped on regions with a high probability of representing a pedestrian. We have integrated our method in a generic framework for pedestrian detection [1] and we have studied two aspects: (1)how the performance of the algorithms varies with respect to the filters proposed by our pruning strategy and (2) what is the speed gain when the quality loss is negligible. The proposed filters have a negligible loss in performance and even improve it in some cases while the execution time is improved.