{"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/filtered-channel-features-for-pedestrian","title":"Filtered Channel Features for Pedestrian Detection","arxiv_id":"1501.05759","date":"2015-01-23","proceeding":null,"authors":["Shanshan Zhang","Rodrigo Benenson","Bernt Schiele"],"abstract":"This paper starts from the observation that multiple top performing\npedestrian detectors can be modelled by using an intermediate layer filtering\nlow-level features in combination with a boosted decision forest. Based on this\nobservation we propose a unifying framework and experimentally explore\ndifferent filter families. We report extensive results enabling a systematic\nanalysis.\n  Using filtered channel features we obtain top performance on the challenging\nCaltech and KITTI datasets, while using only HOG+LUV as low-level features.\nWhen adding optical flow features we further improve detection quality and\nreport the best known results on the Caltech dataset, reaching 93% recall at 1\nFPPI.","url_abs":"http://arxiv.org/abs/1501.05759v1","url_pdf":"http://arxiv.org/pdf/1501.05759v1.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":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pedestrian-detection-on-caltech","task":"Pedestrian Detection","dataset":"Caltech","model":"Checkerboards+","rank_in_archive_order":29,"of":33,"metrics":{"Reasonable Miss Rate":"17.1"},"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}