Acceleration of Vanishing Point-Based Line Sampling Scheme for People Localization and Height Estimation via 3D Line Sampling

Abstract

With the popularity of vision-based camera surveillance, the research on people localization appeals to much attention. In this paper, we propose an efficient and effective system capable of locating a crowd of dense people in real time, using multiple cameras. For each camera view, sample lines, originated from a vanishing point, of foreground objects are projected on the ground plane. Ground regions containing a high density of projected lines are then used to find people locations. Enhanced from previous works, the people localization approach proposed in this paper needs not project all foreground pixels of all views to multiple reference planes or compute pairwise intersections of projected sample lines at different heights, resulting in significant improvement in computational efficiency. Furthermore, the people heights can also be estimated. Experimental results on real surveillance scenes show that comparable accuracy in people localization can be achieved with five times in computing speed compared with our previous approach.