{"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/unsupervised-tube-extraction-using","title":"Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories","arxiv_id":null,"date":"2015-12-01","proceeding":"ICCV 2015 12","authors":["Mihai Marian Puscas","Enver Sangineto","Dubravko Culibrk","Nicu Sebe"],"abstract":"We address the problem of automatic extraction of foreground objects from videos. The goal is to provide a method for unsupervised collection of samples  which can be further used for object detection training without any human intervention. We use the well known Selective Search approach to produce an initial still-image based segmentation of the video frames.   This initial set of proposals is pruned and temporally extended using optical flow and transductive learning.    Specifically, we propose to  use Dense Trajectories in order to robustly match and track candidate boxes over different frames. The obtained box tracks are used to collect samples for  unsupervised training of track-specific detectors. Finally, the detectors are run on the videos  to extract the final tubes. The combination of appearance-based static ''objectness'' (Selective Search), motion information (Dense Trajectories) and transductive learning (detectors are forced to \"overfit\" on the unsupervised data used for training) makes the proposed approach extremely robust. We  outperform state-of-the-art systems by a large margin on common benchmarks used for tube proposal evaluation. ","url_abs":"http://openaccess.thecvf.com/content_iccv_2015/html/Puscas_Unsupervised_Tube_Extraction_ICCV_2015_paper.html","url_pdf":"http://openaccess.thecvf.com/content_iccv_2015/papers/Puscas_Unsupervised_Tube_Extraction_ICCV_2015_paper.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":"unsupervised-tube-extraction-using","repo_url":"https://github.com/mihaipuscas/unsupervised-tube-extraction","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":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"transductive-learning","task_name":"Transductive Learning"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"selective-search","method_name":"Selective Search"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}