{"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/temporal-activity-detection-in-untrimmed","title":"Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks","arxiv_id":"1608.08128","date":"2016-08-29","proceeding":null,"authors":["Alberto Montes","Amaia Salvador","Santiago Pascual","Xavier Giro-i-Nieto"],"abstract":"This thesis explore different approaches using Convolutional and Recurrent\nNeural Networks to classify and temporally localize activities on videos,\nfurthermore an implementation to achieve it has been proposed. As the first\nstep, features have been extracted from video frames using an state of the art\n3D Convolutional Neural Network. This features are fed in a recurrent neural\nnetwork that solves the activity classification and temporally location tasks\nin a simple and flexible way. Different architectures and configurations have\nbeen tested in order to achieve the best performance and learning of the video\ndataset provided. In addition it has been studied different kind of post\nprocessing over the trained network's output to achieve a better results on the\ntemporally localization of activities on the videos. The results provided by\nthe neural network developed in this thesis have been submitted to the\nActivityNet Challenge 2016 of the CVPR, achieving competitive results using a\nsimple and flexible architecture.","url_abs":"http://arxiv.org/abs/1608.08128v3","url_pdf":"http://arxiv.org/pdf/1608.08128v3.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":"temporal-activity-detection-in-untrimmed","repo_url":"https://github.com/imatge-upc/activitynet-2016-cvprw","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"temporal-activity-detection-in-untrimmed","repo_url":"https://github.com/MuhammadAsadJaved/asad","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"temporal-activity-detection-in-untrimmed","repo_url":"https://github.com/vohoaiviet/activitynet-2016-cvprw","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"activity-detection","task_name":"Activity Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.08128","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}