{"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/fusion-of-multispectral-data-through","title":"Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection","arxiv_id":"1802.09972","date":"2018-02-27","proceeding":null,"authors":["Dayan Guan","Yanpeng Cao","Jun Liang","Yanlong Cao","Michael Ying Yang"],"abstract":"Multispectral pedestrian detection has received extensive attention in recent\nyears as a promising solution to facilitate robust human target detection for\naround-the-clock applications (e.g. security surveillance and autonomous\ndriving). In this paper, we demonstrate illumination information encoded in\nmultispectral images can be utilized to significantly boost performance of\npedestrian detection. A novel illumination-aware weighting mechanism is present\nto accurately depict illumination condition of a scene. Such illumination\ninformation is incorporated into two-stream deep convolutional neural networks\nto learn multispectral human-related features under different illumination\nconditions (daytime and nighttime). Moreover, we utilized illumination\ninformation together with multispectral data to generate more accurate semantic\nsegmentation which are used to boost pedestrian detection accuracy. Putting all\nof the pieces together, we present a powerful framework for multispectral\npedestrian detection based on multi-task learning of illumination-aware\npedestrian detection and semantic segmentation. Our proposed method is trained\nend-to-end using a well-designed multi-task loss function and outperforms\nstate-of-the-art approaches on KAIST multispectral pedestrian dataset.","url_abs":"http://arxiv.org/abs/1802.09972v1","url_pdf":"http://arxiv.org/pdf/1802.09972v1.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":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"multispectral-object-detection","task_name":"Multispectral Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multispectral-object-detection-on-kaist","task":"Multispectral Object Detection","dataset":"KAIST Multispectral Pedestrian Detection Benchmark","model":"IATDNN+IASS","rank_in_archive_order":13,"of":17,"metrics":{"All Miss Rate":"48.96"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.09972","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}