Head-Dominant Enhancement With Local Count for Better Human Detection in Crowds

In crowded scenes, it is difficult to extract discriminating human features due to occlusion. Some human detectors have improved this issue by introducing head detection. However, a complex problem still exists in associating full-body detection with its corresponding head detection. Instead of learning the association, we propose a Head-dominant Enhancement Module (HDEM) that uses the full-body proposal to regress the head bounding box. To embed the head information into the target human feature, we further propose a Consistent Weighted (CW) loss. Additionally, existing Non-Maximum Suppression (NMS) algorithms do not consider density changes with the selection of detection boxes, which leads to false and missed detection. Similar to human visual habits in occluded scenarios, we propose a Count-aware Dynamic Threshold Module (CADTM) that utilizes head context information to predict local count, which is associated with crowd density. CADTM can solve the inherent defects of Greedy-NMS in crowded scenes by adjusting the Intersection over Union (IoU) threshold dynamically. Ultimately, through the combination of HDEM and CADTM, we achieve state-of-the-art performance on CrowdHuman with a small computational cost. Our method achieves 4.6% AP gains, 2.2% MR-2 gains, and 3.2% JI gains over a Cascade R-CNN baseline. Furthermore, the proposed method is flexible and can be used with most proposal-based detection frameworks and various IoU-based NMS. Note to Practitioners—The motivation for this study arises from a prevalent issue encountered in human detection applications, particularly in densely populated areas such as shopping malls, streets, and subway stations, where occlusion poses a significant challenge. Employing a generic object detector results in numerous missed detections, thereby significantly compromising the overall performance of the detector. This research proposes a convenient plugin that achieves significant performance improvements at a very small cost. To validate its efficacy, our proposed method is thoroughly evaluated on proposal-based detection frameworks. The experimental results demonstrate the robustness of our approach and its ability to adapt to diverse crowded scenarios.