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The efficiency of the system is affected by real\u2010time processing and the error rate of detection. These concerns have not been completely addressed in previous studies. Therefore, this study proposes a real\u2010time pedestrian recognition system that ensures high accuracy by using a deep learning classifier and zebra\u2010crossing recognition techniques. The proposed system was designed to improve pedestrian safety and reduce accidents at intersections. Environmental feature vectors were first used to detect zebra crossings and to determine crossing areas. An adaptive mapping technique was then used to map the pedestrian waiting area based on the crossing area. A dual camera mechanism was used to maintain detection accuracy and improve system fault tolerance. Finally, the you\u2010only\u2010look\u2010once model was used to recognize pedestrians at intersections. A system prototype was implemented to verify the feasibility of the proposed system. The results revealed that the proposed scheme outperforms the conventional histogram of oriented gradients and Haarcascade schemes.<\/jats:p>","DOI":"10.1002\/spe.2742","type":"journal-article","created":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T01:21:21Z","timestamp":1565745681000},"page":"630-644","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A crosswalk pedestrian recognition system by using deep learning and zebra\u2010crossing recognition techniques"],"prefix":"10.1002","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0978-9576","authenticated-orcid":false,"given":"Chyi\u2010Ren","family":"Dow","sequence":"first","affiliation":[{"name":"Department of Information Engineering and Computer Science Feng Chia University  Taichung Taiwan"}]},{"given":"Huu\u2010Huy","family":"Ngo","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science Feng Chia University  Taichung Taiwan"}]},{"given":"Liang\u2010Hsuan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science Feng Chia University  Taichung Taiwan"}]},{"given":"Po\u2010Yu","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science Feng Chia University  Taichung Taiwan"}]},{"given":"Kuan\u2010Chieh","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science Feng Chia University  Taichung Taiwan"}]},{"given":"Van\u2010Tung","family":"Bui","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science Feng Chia University  Taichung Taiwan"}]}],"member":"311","published-online":{"date-parts":[[2019,8,13]]},"reference":[{"volume-title":"Discussion Guide for Automated and Connected Vehicles, Pedestrians, and Bicyclists","year":"2017","author":"Sandt L","key":"e_1_2_7_2_1"},{"key":"e_1_2_7_3_1","unstructured":"NajadaHA MahgoubI.Big vehicular traffic data mining: towards accident and congestion prevention. 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