Pedestrian detection from salient regions

Classic algorithms of pedestrian detection usually locate the latent position via sliding window techniques, which resize the matching window and/or original images at different scales and scan the image. However, this method has two main drawbacks. First, resizing at a fix rate cannot search through the whole scale space, resulting in the failure of accurate object location. Second, resizing and scanning at various scales is usually time-consuming, which is improper for practical applications. To conquer the above difficulties, a novel pedestrian detection method with salient information is proposed. In this paper, the salient detection model and the traditional covariance matrix descriptor are combined in a Bayesian framework to detect pedestrians in the still image. Finally, the efficiency of our approach compared with state-of-the-art results is demonstrated on the public INRIA dataset.