MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

Abstract

Crowd counting aims to count the number of people in crowded scenes, which is important to the security systems, traffic control and so on. The existing methods typically using local features cannot properly handle the perspective distortion and the varying scales in congested scene images, and henceforth perform wrong people counting. To alleviate this issue, this study proposes a multi-scale residual feature-aware network (MSR-FAN) that combines multi-scale features using multiple receptive field sizes and learns the featureaware information on each image. The MSR-FAN is trained end-to-end to generate highquality density map and evaluate the crowd number. The method consists of three parts. To handle the perspective changes problem, the first part, the direction-based featureenhanced network, is designed to encode the perspective information in four directions based on the initial image feature. The second part, the proposed multi-scale residual block module, gets the global information to handle the represent the regional feature better. This module explores features of different scales as well as reinforce the global feature. The third part, the feature-aware block, is designed to extract the feature hidden in the different channels. Experiment results based on benchmark datasets show that the proposed approach outperforms the existing state-of-the-art methods.

Counting dense object of multiple types based on feature enhancement

“…The module finely encodes the feature map after initial feature extraction in four directions (U, D, L, and R) on the length and width planes. This operation extracts as much spatial information as possible that is hidden due to perspective distortion effects, and in the counting task, the density distribution features of vehicles or people are also included in this spatial information ( Zhao et al, 2021 ). As shown in Figure 2 , the SCM module first slices the input feature map f i from bottom to top in the H direction.…”

“…Dense object counting is mainly used to calculate the number of dense objects in images or videos, such as the number of vehicles in traffic jam scenes ( Min et al, 2022 ), the crowd counting in congested public scenes ( Zhao et al, 2021 ), the number of specific bacteria or cells in microscopic scenes ( Fan et al, 2022 ), and the goods on shelves in indoor packed scenes ( Goldman et al, 2019 ). The achievements of dense object counting research have been widely used in the fields of traffic flow prediction, public safety management, biology and medicine-pharmacy, and supermarket monitoring and management.…”