{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/person-re-identification-in-the-wild","title":"Person Re-identification in the Wild","arxiv_id":"1604.02531","date":"2016-04-09","proceeding":"CVPR 2017 7","authors":["Liang Zheng","Hengheng Zhang","Shaoyan Sun","Manmohan Chandraker","Yi Yang","Qi Tian"],"abstract":"We present a novel large-scale dataset and comprehensive baselines for\nend-to-end pedestrian detection and person recognition in raw video frames. Our\nbaselines address three issues: the performance of various combinations of\ndetectors and recognizers, mechanisms for pedestrian detection to help improve\noverall re-identification accuracy and assessing the effectiveness of different\ndetectors for re-identification. We make three distinct contributions. First, a\nnew dataset, PRW, is introduced to evaluate Person Re-identification in the\nWild, using videos acquired through six synchronized cameras. It contains 932\nidentities and 11,816 frames in which pedestrians are annotated with their\nbounding box positions and identities. Extensive benchmarking results are\npresented on this dataset. Second, we show that pedestrian detection aids\nre-identification through two simple yet effective improvements: a\ndiscriminatively trained ID-discriminative Embedding (IDE) in the person\nsubspace using convolutional neural network (CNN) features and a Confidence\nWeighted Similarity (CWS) metric that incorporates detection scores into\nsimilarity measurement. Third, we derive insights in evaluating detector\nperformance for the particular scenario of accurate person re-identification.","url_abs":"http://arxiv.org/abs/1604.02531v2","url_pdf":"http://arxiv.org/pdf/1604.02531v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"person-recognition","task_name":"Person Recognition"}],"methods":[],"datasets_introduced":[{"slug":"prw","name":"PRW","full_name":"Person Re-identification in the Wild"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1604.02531","atlas_url":"https://app.syntology.ai/?focus=1604.02531","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}