{"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/siamfc-towards-robust-and-accurate-visual","title":"SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines","arxiv_id":"1911.06188","date":"2019-11-14","proceeding":null,"authors":["Yinda Xu","Zeyu Wang","Zuoxin Li","Ye Yuan","Gang Yu"],"abstract":"Visual tracking problem demands to efficiently perform robust classification and accurate target state estimation over a given target at the same time. Former methods have proposed various ways of target state estimation, yet few of them took the particularity of the visual tracking problem itself into consideration. After a careful analysis, we propose a set of practical guidelines of target state estimation for high-performance generic object tracker design. Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch(G1), classification score without ambiguity(G2), tracking without prior knowledge(G3), and estimation quality score(G4). Extensive analysis and ablation studies demonstrate the effectiveness of our proposed guidelines. Without bells and whistles, our SiamFC++ tracker achieves state-of-the-art performance on five challenging benchmarks(OTB2015, VOT2018, LaSOT, GOT-10k, TrackingNet), which proves both the tracking and generalization ability of the tracker. Particularly, on the large-scale TrackingNet dataset, SiamFC++ achieves a previously unseen AUC score of 75.4 while running at over 90 FPS, which is far above the real-time requirement. Code and models are available at: https://github.com/MegviiDetection/video_analyst .","url_abs":"https://arxiv.org/abs/1911.06188v4","url_pdf":"https://arxiv.org/pdf/1911.06188v4.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":"siamfc-towards-robust-and-accurate-visual","repo_url":"https://github.com/MegviiDetection/video_analyst","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"siamfc-towards-robust-and-accurate-visual","repo_url":"https://github.com/ZekuiQin/SiamKPN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"siamfc-towards-robust-and-accurate-visual","repo_url":"https://github.com/mingyuw/tracker_209as","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"siamfc-towards-robust-and-accurate-visual","repo_url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SiamFC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"siamfc-towards-robust-and-accurate-visual","repo_url":"https://github.com/2023-MindSpore-4/Code11/tree/main/SiamFC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"siamfc-towards-robust-and-accurate-visual","repo_url":"https://github.com/code-implementation1/Code8/tree/main/SiamFC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"robust-classification","task_name":"Robust classification"},{"task_slug":"state-estimation","task_name":"State Estimation"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"},{"task_slug":"visual-tracking","task_name":"Visual Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-object-tracking-on-got-10k","task":"Visual Object Tracking","dataset":"GOT-10k","model":"SiamFC++","rank_in_archive_order":42,"of":42,"metrics":{"Average Overlap":"61.0","Success Rate 0.5":"74.2"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-trackingnet","task":"Visual Object Tracking","dataset":"TrackingNet","model":"SiamFC++","rank_in_archive_order":31,"of":40,"metrics":{"Accuracy":"74.5","Normalized Precision":"79.8","Precision":"68.5"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-vot201718","task":"Visual Object Tracking","dataset":"VOT2017/18","model":"SiamFC++","rank_in_archive_order":2,"of":15,"metrics":{"Expected Average Overlap (EAO)":"0.428"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/1911.06188","atlas_url":"https://app.syntology.ai/?focus=1911.06188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06188"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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