{"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/multi-view-people-detection-in-large-scenes","title":"Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting","arxiv_id":"2405.19943","date":"2024-05-30","proceeding":null,"authors":["Qi Zhang","Yunfei Gong","Daijie Chen","Antoni B. Chan","Hui Huang"],"abstract":"Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets. However, current methods are mainly trained and evaluated on small, single scenes with a limited number of multi-view frames and fixed camera views. As a result, these methods may not be practical for detecting people in larger, more complex scenes with severe occlusions and camera calibration errors. This paper focuses on improving multi-view people detection by developing a supervised view-wise contribution weighting approach that better fuses multi-camera information under large scenes. Besides, a large synthetic dataset is adopted to enhance the model's generalization ability and enable more practical evaluation and comparison. The model's performance on new testing scenes is further improved with a simple domain adaptation technique. Experimental results demonstrate the effectiveness of our approach in achieving promising cross-scene multi-view people detection performance. See code here: https://vcc.tech/research/2024/MVD.","url_abs":"https://arxiv.org/abs/2405.19943v1","url_pdf":"https://arxiv.org/pdf/2405.19943v1.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":"multi-view-people-detection-in-large-scenes","repo_url":"https://github.com/zqyq/Multi-view-People-Detection-in-Large-Scenes-via-View-wise-Contribution-Weighting","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"camera-calibration","task_name":"Camera Calibration"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"multiview-detection","task_name":"Multiview Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multiview-detection-on-cvcs","task":"Multiview Detection","dataset":"CVCS","model":"SVCW","rank_in_archive_order":2,"of":6,"metrics":{"F1_score (0.5m)":"/","F1_score (1m)":"68.4","MODA (0.5m)":"/","MODA (1m)":"46.2","MODP (1m)":"78.4","Precision (1m)":"81.2","Recall (1m)":"59.1"},"uses_additional_data":false},{"leaderboard":"/sota/multiview-detection-on-citystreet","task":"Multiview Detection","dataset":"CityStreet","model":"SVCW","rank_in_archive_order":3,"of":5,"metrics":{"F1_score (2m)":"76.0","MODA (2m)":"55.0","MODP (2m)":"70.0","Precision (2m)":"81.4","Recall (2m)":"71.2"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2405.19943","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}