  Person reidentification (ReID) refers to the task of verifying the identity
of a pedestrian observed from nonoverlapping views in a surveillance camera
network. It has recently been validated that reranking can achieve remarkable
performance improvements in person ReID systems. However, current reranking
approaches either require feedback from users or suffer from burdensome
computational costs. In this paper, we propose to exploit a density-adaptive
smooth kernel technique to achieve efficient and effective reranking.
Specifically, we adopt a smooth kernel function to formulate the neighbor
relationships among data samples with a density-adaptive parameter. Based on
this new formulation, we present two simple yet effective reranking methods,
termed \emph{inverse} density-adaptive kernel based reranking (inv-DAKR) and
\emph{bidirectional} density-adaptive kernel based reranking (bi-DAKR), in
which the local density information in the vicinity of each gallery sample is
elegantly exploited. Moreover, we extend the proposed inv-DAKR and bi-DAKR
methods to incorporate the available extra probe samples and demonstrate that
when and why these extra probe samples are able to improve the local
neighborhood and thus further refine the ranking results. Extensive experiments
are conducted on six benchmark datasets, including: PRID450s, VIPeR, CUHK03,
GRID, Market-1501 and Mars. The experimental results demonstrate that our
proposals are effective and efficient.
