{"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/increasing-pedestrian-detection-performance","title":"Increasing pedestrian detection performance through weighting of detection impairing factors","arxiv_id":null,"date":"2022-12-08","proceeding":"ACM Computer Science in Cars Symposium 2022 12","authors":["Korbinian Hagn","Oliver Grau"],"abstract":"Object detection is a matured technique, converging to the detection performance of human vision. This paper presents a method to further close the remaining gap of detection capability by investigating visual factors impairing the detectability of objects. As some of these factors are hard or impossible to measure in real sensor data, a detector is trained on synthetic data making perfect\r\nmeasurements and ground truth data available at a large scale. The resulting detector is then used to calibrate an empirical weighting\r\nloss, which weights samples of real training data and their corresponding detection impairing factors. The method is applied to the task of pedestrian detection in traffic scenes. The effectiveness of the empirical detection impairment weighting loss (DIW loss)\r\nis demonstrated on a detector trained on the CityPersons dataset and reaches a new state-of-the-art detection performance on this\r\nbenchmark, improving the previous by 1.88%.","url_abs":"https://dl.acm.org/doi/pdf/10.1145/3568160.3570225","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3568160.3570225","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-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":[{"leaderboard":"/sota/pedestrian-detection-on-citypersons","task":"Pedestrian Detection","dataset":"CityPersons","model":"DIW Loss","rank_in_archive_order":1,"of":22,"metrics":{"Heavy MR^-2":"28.37","Reasonable MR^-2":"6.23","Small MR^-2":"7.36"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}