RDEPD: Re-Exploring Depth Estimation for Pedestrian Detection

Pedestrian detection is a fundamental task in computer vision field. It remains challenging due to perspective affine and object occlusion. To alleviate these problems, this paper re-thinks the assistance of depth estimation, and then proposes a novel pedestrian detection method named RDEPD. The method consists of a data augmentation module based on depth (DamD) and a detection framework with learnable attention module based on depth(LAmD) and self-suitable NMS(S
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NMS). DamD provides hard examples with mosaics at the depth gradient, which can improve the generalization ability. The detection framework pays more attention on these instances with occlusion or perspective affine by LamD and S
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NMS. Where LAmD is responsible for integrating depth and RGB cues to guide localization, and S
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NMS exploits every possible predicting box to improve the detecting precision. Extensive experimental results demonstrate that the proposed RDEPD significantly outperforms most state-of-the-art methods on the authoritative MOT17det, CrowdHuman, and Citypersons datasets, especially for occlusion situations.