{"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/disparity-sliding-window-object-proposals","title":"Disparity Sliding Window: Object Proposals From Disparity Images","arxiv_id":"1805.06830","date":"2018-05-17","proceeding":null,"authors":["Julian Müller","Andreas Fregin","Klaus Dietmayer"],"abstract":"Sliding window approaches have been widely used for object recognition tasks\nin recent years. They guarantee an investigation of the entire input image for\nthe object to be detected and allow a localization of that object. Despite the\ncurrent trend towards deep neural networks, sliding window methods are still\nused in combination with convolutional neural networks. The risk of overlooking\nan object is clearly reduced compared to alternative detection approaches which\ndetect objects based on shape, edges or color. Nevertheless, the sliding window\ntechnique strongly increases the computational effort as the classifier has to\nverify a large number of object candidates. This paper proposes a sliding\nwindow approach which also uses depth information from a stereo camera. This\nleads to a greatly decreased number of object candidates without significantly\nreducing the detection accuracy. A theoretical investigation of the\nconventional sliding window approach is presented first. Other publications to\ndate only mentioned rough estimations of the computational cost. A mathematical\nderivation clarifies the number of object candidates with respect to parameters\nsuch as image and object size. Subsequently, the proposed disparity sliding\nwindow approach is presented in detail. The approach is evaluated on pedestrian\ndetection with annotations and images from the KITTI object detection\nbenchmark. Furthermore, a comparison with two state-of-the-art methods is made.\nCode is available in C++ and Python https://github.com/julimueller/\ndisparity-sliding-window.","url_abs":"http://arxiv.org/abs/1805.06830v2","url_pdf":"http://arxiv.org/pdf/1805.06830v2.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":"disparity-sliding-window-object-proposals","repo_url":"https://github.com/julimueller/disparity-sliding-window","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"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}