Saliency-Based Deformable Model for Pedestrian Detection

Pedestrian detection, which is to identify category pedestrian of object and give the position information in the image, is an important and yet challenging task due to the intra-class variation of pedestrians in clothing and articulation. Previous researches mainly focus on feature extraction and sliding window, where the former aims to find robust feature representation while the latter seeks to locate the latent position. However, most of sliding windows are based on scale transformation and traverse the entire image. Therefore, it will bring computational complexity and false detection which is not necessary. To conquer the above difficulties, we propose a novel Saliency-Based Deformable Model SBDM method for pedestrian detection. In SBDM method we present that, besides the local features, the saliency in the image provides important constraints that are not yet well utilized. And a probabilistic framework is proposed to model the relationship between Saliency detection and the feature Deformable Model via a Bayesian rule to detect pedestrians in the still image.