Self-Guided Body Part Alignment With Relation Transformers for Occluded Person Re-Identification

Person re-identification in the wild is often challenged by occlusion. Existing methods mainly rely on learned external cues like pose or parsing to ease occlusion distraction. This knowledge highly related to body semantics may introduce alignment effects, leading to additional requirements for dedicated training data and inference computation. We propose the Self-guided Body Part Alignment method that learns cue-free semantic-aligned local prediction for feature representations to avoid high-cost dependence on external cues. First, scale-wise global spatial attention is utilized to determine essential body parts automatically. A relation transformer network is then employed to predict semantic-aligned local parts, guided with anchored global information by constraint loss. Similarity metrics for all parts are merged with threshold conditions to filter invisible body parts comprehensively. Experimental results on occluded and holistic person reID benchmarks show the proposed method outperforms other cue-relied and cue-free methods. As far as we know, this is the first method that applies transformer networks on local predictions for occluded reID tasks.

Automated summary

This method introduces the Self-guided Body Part Alignment technique, which avoids high dependence on external cues and performs well in occluded and holistic person re-identification tasks.