{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:27:47Z","timestamp":1729225667889,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>In this paper, we show that exploiting Generative Adversarial Networks (GANs) to transform nighttime images into daytime representation increases the robustness of pedestrian detection in low-light conditions. Our work aims at first learning the image translation to transfer the style from daytime images to nighttime images with unpaired GAN training. Second, we use our end-to-end trained GAN model to translate night images as a pre-processing step before feeding them into an object detector that is pre-trained on daytime images only. To demonstrate the effectiveness of our translation approach, we conducted experiments on two real-world pedestrian datasets using both one-stage and two-stage object detectors. Our results outperform the baseline in all experiments and show highly competitive detection performance compared with other GAN-based approaches while holding the most lightweight architecture. We believe that our approach is an effective pre-processing first step that helps in bridging the performance gap between day and night at no expense of re-training object detector networks with more night images.<\/jats:p>","DOI":"10.3233\/faia240542","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:47:42Z","timestamp":1729169262000},"source":"Crossref","is-referenced-by-count":0,"title":["Night-to-Day: Unpaired Image-to-Image Translation for Nighttime Pedestrian Detection"],"prefix":"10.3233","author":[{"given":"Afnan","family":"Althoupety","sequence":"first","affiliation":[{"name":"Portland State University, Portland, USA"},{"name":"Taif University, Taif, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Yun","family":"Wang","sequence":"additional","affiliation":[{"name":"Portland State University, Portland, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wu-Chi","family":"Feng","sequence":"additional","affiliation":[{"name":"Portland State University, Portland, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Banafsheh","family":"Rekabdar","sequence":"additional","affiliation":[{"name":"Portland State University, Portland, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240542","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:47:43Z","timestamp":1729169263000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240542","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}