{"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/scene-specific-pedestrian-detection-based-on","title":"Scene-Specific Pedestrian Detection Based on Parallel Vision","arxiv_id":"1712.08745","date":"2017-12-23","proceeding":null,"authors":["Wenwen Zhang","Kunfeng Wang","Hua Qu","Jihong Zhao","Fei-Yue Wang"],"abstract":"As a special type of object detection, pedestrian detection in generic scenes\nhas made a significant progress trained with large amounts of labeled training\ndata manually. While the models trained with generic dataset work bad when they\nare directly used in specific scenes. With special viewpoints, flow light and\nbackgrounds, datasets from specific scenes are much different from the datasets\nfrom generic scenes. In order to make the generic scene pedestrian detectors\nwork well in specific scenes, the labeled data from specific scenes are needed\nto adapt the models to the specific scenes. While labeling the data manually\nspends much time and money, especially for specific scenes, each time with a\nnew specific scene, large amounts of images must be labeled. What's more, the\nlabeling information is not so accurate in the pixels manually and different\npeople make different labeling information. In this paper, we propose an\nACP-based method, with augmented reality's help, we build the virtual world of\nspecific scenes, and make people walking in the virtual scenes where it is\npossible for them to appear to solve this problem of lacking labeled data and\nthe results show that data from virtual world is helpful to adapt generic\npedestrian detectors to specific scenes.","url_abs":"http://arxiv.org/abs/1712.08745v1","url_pdf":"http://arxiv.org/pdf/1712.08745v1.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":"scene-specific-pedestrian-detection-based-on","repo_url":"https://github.com/rbgirshick/voc-dpm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}