CrowdFPN: crowd counting via scale-enhanced and location-aware feature pyramid network

Abstract

Crowd counting has emerged as a prevalent research direction within computer vision, focusing on estimating the number of pedestrians in images or videos. However, existing methods tend to ignore crowd location information and model efficiency, leading to reduced accuracy due to challenges such as multi-scale variations and intricate background interferences. To address these issues, we propose the scale-enhanced and location-aware feature pyramid network for crowd counting (CrowdFPN). First, it can fine-tune each feature layer to focus more on crowd objects within a specific scale through the Scale Enhancement Module. Then, feature information from different layers is effectively fused using the lightweight Adaptive Bi-directional Feature Pyramid Network. Recognizing the importance of crowd location information for accurate counting, we introduce the Location Awareness Module, which embeds crowd location data into the channel attention mechanism while mitigating the effects of complex background interference. Finally, extensive experiments on four popular crowd counting datasets demonstrate the effectiveness of the proposed model. The code is available at https://github.com/zf990312/CrowdFPN.