{"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/object-tracking-by-jointly-exploiting-frame","title":"Object Tracking by Jointly Exploiting Frame and Event Domain","arxiv_id":"2109.09052","date":"2021-09-19","proceeding":"ICCV 2021 10","authors":["Jiqing Zhang","Xin Yang","Yingkai Fu","Xiaopeng Wei","BaoCai Yin","Bo Dong"],"abstract":"Inspired by the complementarity between conventional frame-based and bio-inspired event-based cameras, we propose a multi-modal based approach to fuse visual cues from the frame- and event-domain to enhance the single object tracking performance, especially in degraded conditions (e.g., scenes with high dynamic range, low light, and fast-motion objects). The proposed approach can effectively and adaptively combine meaningful information from both domains. Our approach's effectiveness is enforced by a novel designed cross-domain attention schemes, which can effectively enhance features based on self- and cross-domain attention schemes; The adaptiveness is guarded by a specially designed weighting scheme, which can adaptively balance the contribution of the two domains. To exploit event-based visual cues in single-object tracking, we construct a large-scale frame-event-based dataset, which we subsequently employ to train a novel frame-event fusion based model. Extensive experiments show that the proposed approach outperforms state-of-the-art frame-based tracking methods by at least 10.4% and 11.9% in terms of representative success rate and precision rate, respectively. Besides, the effectiveness of each key component of our approach is evidenced by our thorough ablation study.","url_abs":"https://arxiv.org/abs/2109.09052v1","url_pdf":"https://arxiv.org/pdf/2109.09052v1.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":"object-tracking-by-jointly-exploiting-frame","repo_url":"https://github.com/xinli-zn/informative-tracking-benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"object-tracking-by-jointly-exploiting-frame","repo_url":"https://github.com/Jee-King/ICCV2021_Event_Frame_Tracking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"}],"methods":[],"datasets_introduced":[{"slug":"fe108","name":"FE108","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-tracking-on-fe108","task":"Object Tracking","dataset":"FE108","model":"Multi-modal","rank_in_archive_order":3,"of":8,"metrics":{"Averaged Precision":"92.4","Success Rate":"63.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.09052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.09052"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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