High quality proposal feature generation for crowded pedestrian detection

Occlusion is a severe problem for pedestrian detection in crowded scenes. Due to the diversity of pedestrian postures and occlusion forms, leading to false detection and missed detection. In this paper, we propose a high quality proposal feature generation pedestrian detection algorithm to improve detection performance. Firstly, Dual-Region Feature Generation (DRFG) is proposed to generate high quality proposal features. Specifically, visible regions with less occlusion are introduced and low-precision proposals are generated for both the full-body and visible regions respectively. Then, proposals are respectively selected from the two kinds of proposals mentioned above to match in pairs, so as to guarantee a strong correspondence in information between the two proposals. Afterwards, the successfully matched proposal features are fused by Selective Kernel Feature Fusion (SKFF) to generate high quality proposal features. Secondly, Paired Multiple Instance Prediction(PMIP) is performed on the fused features to generate multiple prediction branches, and each prediction branch generates full-body and visible prediction box. Finally, Paired Non-Maximum Suppression(PNMS) is applied to the prediction boxes to reduce the false positives. Experiments have been conducted on CrowdHuman [1] and CityPersons [2] datasets. Comparing with baseline, our methods have achieved 5.9% AP and 1.5% MR -2 improvement on the above two datasets, sufficiently verifying the effectiveness of our methods in crowded pedestrian detection. (c) 2022 Elsevier Ltd. All rights reserved.