{"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/hierarchical-attentive-recurrent-tracking","title":"Hierarchical Attentive Recurrent Tracking","arxiv_id":"1706.09262","date":"2017-06-28","proceeding":"NeurIPS 2017 12","authors":["Adam R. Kosiorek","Alex Bewley","Ingmar Posner"],"abstract":"Class-agnostic object tracking is particularly difficult in cluttered\nenvironments as target specific discriminative models cannot be learned a\npriori. Inspired by how the human visual cortex employs spatial attention and\nseparate \"where\" and \"what\" processing pathways to actively suppress irrelevant\nvisual features, this work develops a hierarchical attentive recurrent model\nfor single object tracking in videos. The first layer of attention discards the\nmajority of background by selecting a region containing the object of interest,\nwhile the subsequent layers tune in on visual features particular to the\ntracked object. This framework is fully differentiable and can be trained in a\npurely data driven fashion by gradient methods. To improve training\nconvergence, we augment the loss function with terms for a number of auxiliary\ntasks relevant for tracking. Evaluation of the proposed model is performed on\ntwo datasets: pedestrian tracking on the KTH activity recognition dataset and\nthe more difficult KITTI object tracking dataset.","url_abs":"http://arxiv.org/abs/1706.09262v2","url_pdf":"http://arxiv.org/pdf/1706.09262v2.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":"hierarchical-attentive-recurrent-tracking","repo_url":"https://github.com/akosiorek/hart","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"}],"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}