{"type":"ScholarlyArticle","title":"When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark","authors":[{"name":"Xiao Wang","slug":"xiao-wang","given":"Xiao","family":"Wang","affiliation":"Wuhan University"},{"name":"Jun Chen","slug":"jun-chen","given":"Jun","family":"Chen","affiliation":"Wuhan University"},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"Wuhan University"},{"name":"Wu Liu","slug":"wu-liu","given":"Wu","family":"Liu","affiliation":"AI Research of JD.com"},{"name":"Shin'ichi Satoh","slug":"shin-ichi-satoh","given":"Shin'ichi","family":"Satoh","affiliation":"National Institute of Informatics"},{"name":"Chao Liang","slug":"chao-liang","given":"Chao","family":"Liang","affiliation":"Wuhan University"},{"name":"Chia-Wen Lin","slug":"chia-wen-lin","given":"Chia-Wen","family":"Lin","affiliation":"National Tsing Hua University"}],"abstract":"Pedestrian detection at nighttime is a crucial and frontier problem in surveillance, but has not been well explored by the computer vision and artificial intelligence communities. Most of existing methods detect pedestrians under favorable lighting conditions (e.g. daytime) and achieve promising performances. In contrast, they often fail under unstable lighting conditions (e.g. nighttime). Night is a critical time for criminal suspects to act in the field of security. The existing nighttime pedestrian detection dataset is captured by a car camera, specially designed for autonomous driving scenarios. The dataset for nighttime surveillance scenario is still vacant. There are vast differences between autonomous driving and surveillance, including viewpoint and illumination. In this paper, we build a novel pedestrian detection dataset from the nighttime surveillance aspect: NightSurveillance1. As a benchmark dataset for pedestrian detection at nighttime, we compare the performances of state-of-the-art pedestrian detectors and the results reveal that the methods cannot solve all the challenging problems of NightSurveillance. We believe that NightSurveillance can further advance the research of pedestrian detection, especially in the field of surveillance security at nighttime.","doi":"10.24963/ijcai.2020/71","url":"https://ijcai.org/proceedings/2020/71","pdf":"https://ijcai.org/proceedings/2020/0071.pdf","markdown":"https://ijcai.org/proceedings/2020/71.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/71","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Computer Vision","track":"Main track","number":71,"pages":{"first":509,"last":515},"keywords":["Computer Vision: Other","Computer Vision: Video: Events, Activities and Surveillance","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24645/file_24645.m3u8","length_sec":276,"provider":"hls"}],"references":[{"position":1,"raw":"[Brazil et al., 2017] Garrick Brazil, Xi Yin, and Xiaoming Liu. 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