CSCNet: A Shallow Single Column Network for Crowd Counting

Abstract

Crowd counting in complex scene is an important but challenge task. The scale variation of crowd makes the shallow network hard to extract effective features. In this paper, we propose a shallow single column network named CSCNet for crowd counting. The key component is complementary scale context block (CSCB). It is designed to capture complementary scale context and obtains a high accuracy with limited depth of the network. As far as we know, CSCNet is the shallowest single column network in existing works. We demonstrate our methods on three challenge benchmarks. Compared to state-of-the-art methods, CSCNet achieves comparable accuracy with much less complexity. CSCNet provides an alternative to achieve comparable or even better performance with about 30% of depth and 50% of width decrease. Besides, CSCNet performs more stably on both sparse and congested crowd scenes.