{"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/pedestrian-detection-in-thermal-images-using","title":"Pedestrian Detection in Thermal Images using Saliency Maps","arxiv_id":"1904.06859","date":"2019-04-15","proceeding":null,"authors":["Debasmita Ghose","Shasvat Mukeshkumar Desai","Sneha Bhattacharya","Deep Chakraborty","Madalina Fiterau","Tauhidur Rahman"],"abstract":"Thermal images are mainly used to detect the presence of people at night or\nin bad lighting conditions, but perform poorly at daytime. To solve this\nproblem, most state-of-the-art techniques employ a fusion network that uses\nfeatures from paired thermal and color images. Instead, we propose to augment\nthermal images with their saliency maps, to serve as an attention mechanism for\nthe pedestrian detector especially during daytime. We investigate how such an\napproach results in improved performance for pedestrian detection using only\nthermal images, eliminating the need for paired color images. For our\nexperiments, we train the Faster R-CNN for pedestrian detection and report the\nadded effect of saliency maps generated using static and deep methods (PiCA-Net\nand R3-Net). Our best performing model results in an absolute reduction of miss\nrate by 13.4% and 19.4% over the baseline in day and night images respectively.\nWe also annotate and release pixel level masks of pedestrians on a subset of\nthe KAIST Multispectral Pedestrian Detection dataset, which is a first publicly\navailable dataset for salient pedestrian detection.","url_abs":"http://arxiv.org/abs/1904.06859v1","url_pdf":"http://arxiv.org/pdf/1904.06859v1.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":[{"paper_slug":"pedestrian-detection-in-thermal-images-using","repo_url":"https://github.com/Information-Fusion-Lab-Umass/Salient-Pedestrian-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"salient-object-detection","task_name":"RGB Salient Object Detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}