{"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/confidence-aware-fusion-using-dempster-shafer","title":"Confidence-aware Fusion using Dempster-Shafer Theory for Multispectral Pedestrian Detection","arxiv_id":null,"date":"2022-03-21","proceeding":"journal 2022 3","authors":["Qing Li; Changqing Zhang; Qinghua Hu; Huazhu Fu; Pengfei Zhu"],"abstract":"Multispectral pedestrian detection is an important and valuable task in many applications, which could provide a more accurate and reliable pedestrian detection result by using the complementary visual information from color and thermal images. However, it faces two open and difficult challenges: 1) how to effectively and dynamically integrate multispectral information according to the confidence of different modalities, and 2) how to produce a reliable prediction result. In this paper, we propose a novel confidence-aware multispectral pedestrian detection (CMPD) method, which flexibly learns the multispectral representation while simultaneously producing a reliable result with confidence estimation. Specifically, a dense fusion strategy is first proposed to extract the multilevel multispectral representation at the feature level. Then, an additional confidence subnetwork is utilized to dynamically estimate the detection confidence for each modality. Finally, Dempster's combination rule is introduced to fuse the results of different branches according to the rectified confidence. Our proposed CMPD method not only effectively integrates multimodal information but also provides a reliable prediction. Extensive experimental results demonstrate the efficiency of our algorithm compared with state-of-the-art methods.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9739079","url_pdf":"https://ieeexplore.ieee.org/abstract/document/9739079","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":"confidence-aware-fusion-using-dempster-shafer","repo_url":"https://github.com/2023-MindSpore-4/Code13/tree/main/zhangchangqing/CMPD-mindspore-main","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"multispectral-object-detection","task_name":"Multispectral Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multispectral-object-detection-on-kaist","task":"Multispectral Object Detection","dataset":"KAIST Multispectral Pedestrian Detection Benchmark","model":"CMPD","rank_in_archive_order":6,"of":17,"metrics":{"All Miss Rate":"28.98"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}