{"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/3d-detnet-a-single-stage-video-based-vehicle","title":"3D-DETNet: a Single Stage Video-Based Vehicle Detector","arxiv_id":"1801.01769","date":"2018-01-05","proceeding":null,"authors":["Suichan Li"],"abstract":"Video-based vehicle detection has received considerable attention over the\nlast ten years and there are many deep learning based detection methods which\ncan be applied to it. However, these methods are devised for still images and\napplying them for video vehicle detection directly always obtains poor\nperformance. In this work, we propose a new single-stage video-based vehicle\ndetector integrated with 3DCovNet and focal loss, called 3D-DETNet. Draw\nsupport from 3D Convolution network and focal loss, our method has ability to\ncapture motion information and is more suitable to detect vehicle in video than\nother single-stage methods devised for static images. The multiple video frames\nare initially fed to 3D-DETNet to generate multiple spatial feature maps, then\nsub-model 3DConvNet takes spatial feature maps as input to capture temporal\ninformation which is fed to final fully convolution model for predicting\nlocations of vehicles in video frames. We evaluate our method on UA-DETAC\nvehicle detection dataset and our 3D-DETNet yields best performance and keeps a\nhigher detection speed of 26 fps compared with other competing methods.","url_abs":"http://arxiv.org/abs/1801.01769v2","url_pdf":"http://arxiv.org/pdf/1801.01769v2.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-detection","task_name":"Object Detection"},{"task_slug":"vehicle-detection","task_name":"vehicle detection"}],"methods":[{"method_slug":"3d-convolution","method_name":"3D Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-ua-detrac","task":"Object Detection","dataset":"UA-DETRAC","model":"3D-DETNet","rank_in_archive_order":9,"of":9,"metrics":{"mAP":"53.30"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}