Partial Feature Aggregation Network for Real-Time Object Counting

Abstract

Object counting has become an important task in computer vision for its practical applications in surveillance system. Previous methods for object counting have achieved promising results in accuracy, but few researchers focus on the real-time performance of counting methods. In this paper, we propose an efficient and accurate light-weight network for object counting, called Partial Feature Aggregation Network (PFANet). In this novel method, a Partial Feature Aggregation (PFA) structure is designed to accelerate networks and improve the utilization of multi-scale features. Moreover, PFANet uses the dilated convolution to enlarge the receptive-filed of network. Experiments on two datasets indicate our network exceeds the existing real-time counting networks in both accuracy and efficiency.