{"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/siamvgg-visual-tracking-using-deeper-siamese","title":"SiamVGG: Visual Tracking using Deeper Siamese Networks","arxiv_id":"1902.02804","date":"2019-02-07","proceeding":null,"authors":["Yuhong Li","Xiaofan Zhang","Deming Chen"],"abstract":"Recently, we have seen a rapid development of Deep Neural Network (DNN) based visual tracking solutions. Some trackers combine the DNN-based solutions with Discriminative Correlation Filters (DCF) to extract semantic features and successfully deliver the state-of-the-art tracking accuracy. However, these solutions are highly compute-intensive, which require long processing time, resulting unsecured real-time performance. To deliver both high accuracy and reliable real-time performance, we propose a novel tracker called SiamVGG\\footnote{https://github.com/leeyeehoo/SiamVGG}. It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking. The architecture of SiamVGG is customized from VGG-16 with the parameters shared by both exemplary images and desired input video frames. We demonstrate the proposed SiamVGG on OTB-2013/50/100 and VOT 2015/2016/2017 datasets with the state-of-the-art accuracy while maintaining a decent real-time performance of 50 FPS running on a GTX 1080Ti. Our design can achieve 2% higher Expected Average Overlap (EAO) compared to the ECO and C-COT in VOT2017 Challenge.","url_abs":"https://arxiv.org/abs/1902.02804v4","url_pdf":"https://arxiv.org/pdf/1902.02804v4.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":"siamvgg-visual-tracking-using-deeper-siamese","repo_url":"https://github.com/leeyeehoo/SiamVGG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"siamvgg-visual-tracking-using-deeper-siamese","repo_url":"https://github.com/logiklesuraj/SiamFC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"siamvgg-visual-tracking-using-deeper-siamese","repo_url":"https://github.com/logiklesuraj/siamfcex","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"siamvgg-visual-tracking-using-deeper-siamese","repo_url":"https://github.com/zllrunning/SiameseX.PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"},{"task_slug":"visual-tracking","task_name":"Visual Tracking"}],"methods":[{"method_slug":"eco","method_name":"ECO"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-object-tracking-on-otb-2013","task":"Visual Object Tracking","dataset":"OTB-2013","model":"SiamVGG","rank_in_archive_order":4,"of":7,"metrics":{"AUC":"0.665"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-otb-2015","task":"Visual Object Tracking","dataset":"OTB-2015","model":"SiamVGG","rank_in_archive_order":15,"of":18,"metrics":{"AUC":"0.654"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-otb-50","task":"Visual Object Tracking","dataset":"OTB-50","model":"SiamVGG","rank_in_archive_order":1,"of":4,"metrics":{"AUC":"0.61"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-vot2016","task":"Visual Object Tracking","dataset":"VOT2016","model":"SiamVGG","rank_in_archive_order":5,"of":6,"metrics":{"Expected Average Overlap (EAO)":"0.351"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-vot2017","task":"Visual Object Tracking","dataset":"VOT2017","model":"SiamVGG","rank_in_archive_order":3,"of":6,"metrics":{"Expected Average Overlap (EAO)":"0.286"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}