Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective

2026-09-16

AI Summary

本文针对遮挡情况下步态识别的问题，提出了一种基于动作检测的专家混合模型（GaitMoE），并通过构建新的遮挡步态数据库OccGait验证了方法的有效性。

Abstract

Extensive occlusions in real-world scenarios pose challenges to gait recognition due to missing and noisy information, as well as body misalignment in position and scale. We argue that rich dynamic contextual information within a gait sequence inherently possesses occlusion-solving traits: 1) Adjacent frames with gait continuity allow holistic body regions to infer occluded body regions; 2) Gait cycles allow information integration between holistic actions and occluded actions. Therefore, we introduce an action detection perspective where a gait sequence is regarded as a composition of actions. To detect accurate actions under complex occlusion scenarios, we propose an Action Detection Based Mixture of Experts (GaitMoE), consisting of Mixture of Temporal Experts (MTE) and Mixture of Action Experts (MAE). MTE adaptively constructs action anchors by temporal experts and MAE adaptively constructs action proposals from action anchors by action experts. Especially, action detection as a proxy task with gait recognition is an end-to-end joint training only with ID labels. In addition, due to the lack of a unified occluded benchmark, we construct a pioneering Occluded Gait database (OccGait), containing rich occlusion scenarios and annotations of occlusion types. Extensive experiments on OccGait, OccCASIA-B,Gait3D and GREW demonstrate the superior performance of GaitMoE.OccGait is available at https://github.com/BNU-IVC/OccGait.

Problem

• Occluded Gait Recognition
• Action Detection
• Mixture of Experts

Innovation

• Action Detection Based Mixture of Experts
• Occluded Gait Recognition
• Mixture of Temporal Experts
• Mixture of Action Experts
• OccGait