Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation

Abstract

This paper proposes a scalable approach for counting pedestrians crossing a virtual line when the crowd is highly dynamic and possibly extremely dense. The approach mainly consists of two parts: local crowd density estimation and pedestrian counting based on accumulating local densities across the line. To obtain a fine estimation of local crowd densities, we divide the neighborhood at the line into a number of blocks. We enforce spatial consistency between local counts in the blocks and those in the enclosing regions to guarantee consistent estimation of local crowd densities. For scalability to various density levels in crowd density estimation, we propose a two-stage strategy: pre-classification of density levels and subsequent regression with overlapped operational ranges. To count pedestrians crossing the virtual line, we accumulate the crowd densities across the line according to the locally estimated velocities. Extensive experimental results demonstrate the effectiveness of the proposed approach and its scalability to crowdedness.