STG: Structured Topology of Gridpoints for Occluded Pedestrian Detection

Abstract

Pedestrian detection in crowds is a challenging problem in computer vision. Existing occlusion-handling methods heavily rely on expensive visible-box annotations to locate visible body parts, posing severe limitations in label acquisition cost and open-world generalization. To break through this limitation, we propose a novel Structured Topology of Gridpoints (STG) framework. Operating strictly under standard full-box annotations without any extra visibility supervision, STG aims to achieve implicit, fine-grained local semantic compensation. Specifically, we formulate a coarse-to-fine reasoning paradigm consisting of three interactive stages. To mitigate the high spatial complexity and eliminate background redundancy, we first introduce a Saliency-Aware Feature Filtering (SAFF) mechanism, which leverages gridpoint heatmaps to filter out low-confidence pedestrian candidates. Second, a query-guided Across-Instance Feature Interaction (AIFI) model is designed to utilize inter-instance spatial relationships to propagate missing context from highly visible individuals to their occluded neighbors. Finally, we devise a prior-guided Inner-Instance Gridpoints Interaction (I2GI) model to achieve fine-grained structured part-level feature completion, which dynamically aggregates vital localized cues from diverse human parts to reconstruct holistic pedestrian representations. Extensive experiments on the CityPersons, CrowdHuman, and WiderPerson datasets demonstrate the effectiveness and efficiency of our proposed method. Specifically, STG achieves a log-average miss rate of 7.41% on Reasonable and 32.05% on Heavy Occlusion subsets of CityPersons, while running at up to 16 FPS, outperforming existing part-based methods under full-box supervision.