{"title":"Learning discriminative local contexts for person re-identification in vehicle surveillance scenarios","authors":"","doi":"10.1007/s10044-024-01219-6","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>In recent years, person re-identification (Re-ID) has been widely used in intelligent surveillance and security. However, Re-ID faces many challenges in the vehicle surveillance scenario, such as heavy occlusion, misalignment, and similar appearances. Most Re-ID methods focus on learning discriminative global features or dividing regions for local feature learning, which may ignore critical but subtle differences between pedestrians. In this paper, we propose a local context aggregation branch for learning discriminative local contexts at multiple scales, which can supplement the critical detailed information omitted in global features. Specifically, we exploit dilated convolutions to simulate spatial feature pyramid to capture multi-scale spatial contexts efficiently. The essential information that can distinguish different pedestrians is then emphasized. Besides, we construct a Re-ID dataset named BSV for vehicle surveillance scenarios and propose a triplet loss with station constraint enhancement, which utilizes additional valuable station information to construct penalty terms to improve the performance of Re-ID further. Extensive experiments are conducted on the proposed BSV dataset and two standard Re-ID datasets, and the results validate the effectiveness of our method.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"1 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Analysis and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10044-024-01219-6","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, person re-identification (Re-ID) has been widely used in intelligent surveillance and security. However, Re-ID faces many challenges in the vehicle surveillance scenario, such as heavy occlusion, misalignment, and similar appearances. Most Re-ID methods focus on learning discriminative global features or dividing regions for local feature learning, which may ignore critical but subtle differences between pedestrians. In this paper, we propose a local context aggregation branch for learning discriminative local contexts at multiple scales, which can supplement the critical detailed information omitted in global features. Specifically, we exploit dilated convolutions to simulate spatial feature pyramid to capture multi-scale spatial contexts efficiently. The essential information that can distinguish different pedestrians is then emphasized. Besides, we construct a Re-ID dataset named BSV for vehicle surveillance scenarios and propose a triplet loss with station constraint enhancement, which utilizes additional valuable station information to construct penalty terms to improve the performance of Re-ID further. Extensive experiments are conducted on the proposed BSV dataset and two standard Re-ID datasets, and the results validate the effectiveness of our method.
期刊介绍：
The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.
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