Object Counting in Video Surveillance Using Multi-scale Density Map Regression

Yi Wang, Junhui Hou, Lap-Pui Chau

Abstract: In this paper, we present an effective convolutional neural network (CNN) for object counting in video surveillance, namely multi-scale density map regressor (MSDMR). In contrast to existing CNN-based methods that achieve high accuracy by means of empirically increasing the model capacity with more complex structures/layers, we focus on a compact CNN. Specifically, the MSDMR is mainly designed with the supervision of multi-scale outputs, in which two CNN stacks estimate coarse- and fine-scale density maps, respectively. The integral of the fine density map provides the count of objects. The two stacks are connected in a cascaded manner and jointly trained such that the overall model can learn discriminative and complementary features to produce expressive performance. Experimental results show that the proposed MSDMR can achieve higher accuracy compared with state-of-the-art methods on the surveillance datasets.