Pedestrian Detection Based on Blob Motion Statistics

Abstract

Pedestrian detection based on video analysis is a key functionality in automated surveillance systems. In this paper, we present efficient detection metrics that consider the fact that human movement presents distinctive motion patterns. Contrary to several methods that perform an intrablob analysis based on motion masks, we approach the problem without necessarily considering the periodic pixel motion inside the blob. As such, we do not analyze periodicity in the pixel luminances, but in the motion statistics of the tracked blob as a whole. For this, we propose the use of the following cues: 1) a cyclic behavior in the blob trajectory, and 2) an in-phase relationship between the change in blob size and position. In addition, we also exploit the relationship between blob size and vertical position, assuming that the camera is positioned sufficiently high. If the homography between the camera and the ground is known, the features are normalized by transforming the blob size to the real person size. For improved performance, we combine these features using the Bayes classifier. We also present a theoretical statistical analysis to evaluate the system performance in the presence of noise. We perform online experiments in a real industrial scenario and also with videos from well-known databases. The results illustrate the applicability of the proposed features.