{"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/is-faster-r-cnn-doing-well-for-pedestrian","title":"Is Faster R-CNN Doing Well for Pedestrian Detection?","arxiv_id":"1607.07032","date":"2016-07-24","proceeding":null,"authors":["Liliang Zhang","Liang Lin","Xiaodan Liang","Kaiming He"],"abstract":"Detecting pedestrian has been arguably addressed as a special topic beyond\ngeneral object detection. Although recent deep learning object detectors such\nas Fast/Faster R-CNN [1, 2] have shown excellent performance for general object\ndetection, they have limited success for detecting pedestrian, and previous\nleading pedestrian detectors were in general hybrid methods combining\nhand-crafted and deep convolutional features. In this paper, we investigate\nissues involving Faster R-CNN [2] for pedestrian detection. We discover that\nthe Region Proposal Network (RPN) in Faster R-CNN indeed performs well as a\nstand-alone pedestrian detector, but surprisingly, the downstream classifier\ndegrades the results. We argue that two reasons account for the unsatisfactory\naccuracy: (i) insufficient resolution of feature maps for handling small\ninstances, and (ii) lack of any bootstrapping strategy for mining hard negative\nexamples. Driven by these observations, we propose a very simple but effective\nbaseline for pedestrian detection, using an RPN followed by boosted forests on\nshared, high-resolution convolutional feature maps. We comprehensively evaluate\nthis method on several benchmarks (Caltech, INRIA, ETH, and KITTI), presenting\ncompetitive accuracy and good speed. Code will be made publicly available.","url_abs":"http://arxiv.org/abs/1607.07032v2","url_pdf":"http://arxiv.org/pdf/1607.07032v2.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":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"region-proposal","task_name":"Region Proposal"},{"task_slug":"object-detection-1","task_name":"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":[{"leaderboard":"/sota/pedestrian-detection-on-caltech","task":"Pedestrian Detection","dataset":"Caltech","model":"RPN+BF","rank_in_archive_order":19,"of":33,"metrics":{"Reasonable Miss Rate":"7.3"},"uses_additional_data":false},{"leaderboard":"/sota/pedestrian-detection-on-caltech","task":"Pedestrian Detection","dataset":"Caltech","model":"FasterRCNN","rank_in_archive_order":22,"of":33,"metrics":{"Reasonable Miss Rate":"8.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1607.07032","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}