Revisiting -Reciprocal Distance Re-Ranking for Skeleton-Based Person Re-Identification
Haocong Rao, Yuan Li, Chunyan Miao
Abstract
Person re-identification (re-ID) as a retrieval task often utilizes a re-ranking model to improve performance. Existing re-ranking methods are typically designed for conventional person re-ID with RGB images, while skeleton representation re-ranking for skeleton-based person re-ID still remains to be explored. To fill this gap, we revisit the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -reciprocal distance re-ranking model in this letter, and propose a generic re-ranking method that exploits the salient skeleton features to perform <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -reciprocal distance encoding for skeleton-based person re-ID re-ranking. In particular, we devise the skeleton sequence pooling to aggregate the most salient features of skeletons within a sequence, and combine both original Euclidean distance and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -reciprocal distance to re-rank the skeleton sequence representations for person re-ID. Furthermore, we propose the context-based Rank-1 voting that jointly exploits the initial ranking list and re-ranking list to vote for the top candidate to enhance the Rank-1 matching. Extensive experiments on three public benchmarks demonstrate that our approach can effectively re-rank different state-of-the-art skeleton representations and significantly improve their person re-ID performance.