{"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/wildtrack-a-multi-camera-hd-dataset-for-dense","title":"WILDTRACK: A Multi-Camera HD Dataset for Dense Unscripted Pedestrian Detection","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Tatjana Chavdarova","Pierre Baqué","Stéphane Bouquet","Andrii Maksai","Cijo Jose","Timur Bagautdinov","Louis Lettry","Pascal Fua","Luc van Gool","François Fleuret"],"abstract":"People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height. The reduction of camera prices allows for the generalization of static multi-camera set-ups. Using joint visual information from multiple synchronized cameras gives the opportunity to improve detection performance.  In this paper, we present a new large-scale and high-resolution dataset. It has been captured with seven static cameras in a public open area, and unscripted dense groups of pedestrians standing and walking. Together with the camera frames, we provide an accurate joint (extrinsic and intrinsic) calibration, as well as 7 series of 400 annotated frames for detection at a rate of 2 frames per second. This results in over 40,000 bounding boxes delimiting every person present in the area of interest, for a total of more than 300 individuals.   We provide a series of benchmark results using baseline algorithms published over the recent months for multi-view detection with deep neural networks, and trajectory estimation using a non-Markovian model.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Chavdarova_WILDTRACK_A_Multi-Camera_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Chavdarova_WILDTRACK_A_Multi-Camera_CVPR_2018_paper.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":"multiview-detection","task_name":"Multiview Detection"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":null,"task_name":"multi-view detection"}],"methods":[],"datasets_introduced":[{"slug":"wildtrack","name":"Wildtrack","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}