{"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/pedestrian-detection-with-autoregressive","title":"Pedestrian Detection with Autoregressive Network Phases","arxiv_id":"1812.00440","date":"2018-12-02","proceeding":"CVPR 2019 6","authors":["Garrick Brazil","Xiaoming Liu"],"abstract":"We present an autoregressive pedestrian detection framework with cascaded\nphases designed to progressively improve precision. The proposed framework\nutilizes a novel lightweight stackable decoder-encoder module which uses\nconvolutional re-sampling layers to improve features while maintaining\nefficient memory and runtime cost. Unlike previous cascaded detection systems,\nour proposed framework is designed within a region proposal network and thus\nretains greater context of nearby detections compared to independently\nprocessed RoI systems. We explicitly encourage increasing levels of precision\nby assigning strict labeling policies to each consecutive phase such that early\nphases develop features primarily focused on achieving high recall and later on\naccurate precision. In consequence, the final feature maps form more peaky\nradial gradients emulating from the centroids of unique pedestrians. Using our\nproposed autoregressive framework leads to new state-of-the-art performance on\nthe reasonable and occlusion settings of the Caltech pedestrian dataset, and\nachieves competitive state-of-the-art performance on the KITTI dataset.","url_abs":"http://arxiv.org/abs/1812.00440v1","url_pdf":"http://arxiv.org/pdf/1812.00440v1.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":"pedestrian-detection-with-autoregressive","repo_url":"https://github.com/garrickbrazil/AR-Ped","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"region-proposal","task_name":"Region Proposal"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.00440","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}