Pedestrian detection in surveillance videos based on CS-LBP feature

Abstract

Detecting different categories of objects in an image and video content is one of the fundamental tasks in computer vision research. Pedestrian detection is a hot research topic, with several applications including robotics, surveillance and automotive safety. Pedestrians are key participants in transportation systems, so pedestrian detection in video surveillance systems is of great significance to the research and application of Intelligent Transportation Systems (ITS). Pedestrian detection is a challenging problem due to the variance of illumination, color, scale, pose, and so forth. Extraction of effictive features is a key to this task. In this work, we present the multi-scale Center-symmetric Local Binary Pattern feature for pedestrian detection. The proposed feature captures gradient information and some texture and scale information. We completed the detection task with a foreground segmentation method. Experiments on CAVIAR sequences show that the proposed feature with support vector machines can detect pedestrians in real-time effectively in surveillance videos.