{"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/deep-convolutional-neural-networks-for-4","title":"Deep convolutional neural networks for pedestrian detection","arxiv_id":"1510.03608","date":"2015-10-13","proceeding":null,"authors":["Denis Tomè","Federico Monti","Luca Baroffio","Luca Bondi","Marco Tagliasacchi","Stefano Tubaro"],"abstract":"Pedestrian detection is a popular research topic due to its paramount\nimportance for a number of applications, especially in the fields of\nautomotive, surveillance and robotics. Despite the significant improvements,\npedestrian detection is still an open challenge that calls for more and more\naccurate algorithms. In the last few years, deep learning and in particular\nconvolutional neural networks emerged as the state of the art in terms of\naccuracy for a number of computer vision tasks such as image classification,\nobject detection and segmentation, often outperforming the previous gold\nstandards by a large margin. In this paper, we propose a pedestrian detection\nsystem based on deep learning, adapting a general-purpose convolutional network\nto the task at hand. By thoroughly analyzing and optimizing each step of the\ndetection pipeline we propose an architecture that outperforms traditional\nmethods, achieving a task accuracy close to that of state-of-the-art\napproaches, while requiring a low computational time. Finally, we tested the\nsystem on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a\nforerunner computational brain of future self-driving cars.","url_abs":"http://arxiv.org/abs/1510.03608v5","url_pdf":"http://arxiv.org/pdf/1510.03608v5.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":"deep-convolutional-neural-networks-for-4","repo_url":"https://github.com/DenisTome/DeepPed","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}