Density-adaptive kernel based efficient reranking approaches for person reidentification
Autor: | Ruopei Guo, Jiaru Lin, Jun Guo, Yonghua Li, Chunguang Li |
---|---|
Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Cognitive Neuroscience Adaptive kernel Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Kernel (linear algebra) 020901 industrial engineering & automation Ranking Artificial Intelligence Kernel (statistics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
Zdroj: | Neurocomputing. 411:91-111 |
ISSN: | 0925-2312 |
Popis: | 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. 39 pages, 18 figures and 12 tables. This paper is an extended version of our preliminary work on ICPR 2018 |
Databáze: | OpenAIRE |
Externí odkaz: |