{"task":"Visual Object Tracking","dataset":"LaSOT-ext","metric_names":["AUC","Normalized Precision","Precision"],"rows":[{"id":48252,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"SAMURAI-L","metrics":{"AUC":"61.0","Normalized Precision":"73.9","Precision":"72.2"},"paper_url":"https://arxiv.org/abs/2411.11922v2","paper_title":"SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory","paper_date":"2024-11-18","code_links":[{"title":"yangchris11/samurai","url":"https://github.com/yangchris11/samurai"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48253,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"DAM4SAM","metrics":{"AUC":"60.9"},"paper_url":"https://arxiv.org/abs/2411.17576v2","paper_title":"A Distractor-Aware Memory for Visual Object Tracking with SAM2","paper_date":"2024-11-26","code_links":[{"title":"jovanavidenovic/dam4sam","url":"https://github.com/jovanavidenovic/dam4sam"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48254,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"LoRAT-L-378","metrics":{"AUC":"56.6","Normalized Precision":"69.0","Precision":"65.1"},"paper_url":"https://arxiv.org/abs/2403.05231v2","paper_title":"Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance","paper_date":"2024-03-08","code_links":[{"title":"litinglin/lorat","url":"https://github.com/litinglin/lorat"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48255,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"LoRAT-g-378","metrics":{"AUC":"56.5","Normalized Precision":"69.0","Precision":"64.9"},"paper_url":"https://arxiv.org/abs/2403.05231v2","paper_title":"Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance","paper_date":"2024-03-08","code_links":[{"title":"litinglin/lorat","url":"https://github.com/litinglin/lorat"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48256,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"UNINEXT-H","metrics":{"AUC":"56.2","Normalized Precision":"63.8","Precision":"63.8"},"paper_url":"https://arxiv.org/abs/2303.06674v2","paper_title":"Universal Instance Perception as Object Discovery and Retrieval","paper_date":"2023-03-12","code_links":[{"title":"MasterBin-IIAU/UNINEXT","url":"https://github.com/MasterBin-IIAU/UNINEXT"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48257,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"MCITrack-L384","metrics":{"AUC":"55.7","Normalized Precision":"66.5","Precision":"62.9"},"paper_url":"https://arxiv.org/abs/2412.11023v1","paper_title":"Exploring Enhanced Contextual Information for Video-Level Object Tracking","paper_date":"2024-12-15","code_links":[{"title":"kangben258/MCITrack","url":"https://github.com/kangben258/MCITrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48258,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"RTracker-L","metrics":{"AUC":"54.9","Normalized Precision":"65.5","Precision":"62.7"},"paper_url":"https://arxiv.org/abs/2403.19242v1","paper_title":"RTracker: Recoverable Tracking via PN Tree Structured Memory","paper_date":"2024-03-28","code_links":[{"title":"norahgreen/rtracker","url":"https://github.com/norahgreen/rtracker"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48259,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"MCITrack-B224","metrics":{"AUC":"54.6","Normalized Precision":"65.7","Precision":"62.1"},"paper_url":"https://arxiv.org/abs/2412.11023v1","paper_title":"Exploring Enhanced Contextual Information for Video-Level Object Tracking","paper_date":"2024-12-15","code_links":[{"title":"kangben258/MCITrack","url":"https://github.com/kangben258/MCITrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48260,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"ODTrack-L","metrics":{"AUC":"53.9"},"paper_url":"https://arxiv.org/abs/2401.01686v1","paper_title":"ODTrack: Online Dense Temporal Token Learning for Visual Tracking","paper_date":"2024-01-03","code_links":[{"title":"gxnu-zhonglab/odtrack","url":"https://github.com/gxnu-zhonglab/odtrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48261,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"ARTrackV2-L","metrics":{"AUC":"53.4","Normalized Precision":"63.7","Precision":"60.2"},"paper_url":"https://arxiv.org/abs/2312.17133v3","paper_title":"ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe","paper_date":"2023-12-28","code_links":[{"title":"miv-xjtu/artrack","url":"https://github.com/miv-xjtu/artrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48262,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"HIPTrack","metrics":{"AUC":"53.0","Normalized Precision":"64.3","Precision":"60.6"},"paper_url":"https://arxiv.org/abs/2311.02072v2","paper_title":"HIPTrack: Visual Tracking with Historical Prompts","paper_date":"2023-11-03","code_links":[{"title":"wenruicai/hiptrack","url":"https://github.com/wenruicai/hiptrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48263,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"ARTrack-L","metrics":{"AUC":"52.8","Normalized Precision":"62.9","Precision":"59.7"},"paper_url":"http://openaccess.thecvf.com//content/CVPR2023/html/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.html","paper_title":"Autoregressive Visual Tracking","paper_date":"2023-01-01","code_links":[{"title":"miv-xjtu/artrack","url":"https://github.com/miv-xjtu/artrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48264,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"DropTrack","metrics":{"AUC":"52.7","Precision":"60.2"},"paper_url":"https://arxiv.org/abs/2304.00571v2","paper_title":"DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks","paper_date":"2023-04-02","code_links":[{"title":"jimmy-dq/dropmae","url":"https://github.com/jimmy-dq/dropmae"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48265,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"ODTrack-B","metrics":{"AUC":"52.4"},"paper_url":"https://arxiv.org/abs/2401.01686v1","paper_title":"ODTrack: Online Dense Temporal Token Learning for Visual Tracking","paper_date":"2024-01-03","code_links":[{"title":"gxnu-zhonglab/odtrack","url":"https://github.com/gxnu-zhonglab/odtrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48266,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"SeqTrack-L384","metrics":{"AUC":"50.7","Normalized Precision":"61.6","Precision":"57.5"},"paper_url":"https://arxiv.org/abs/2304.14394v3","paper_title":"Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking","paper_date":"2023-04-27","code_links":[{"title":"chenxin-dlut/seqtrackv2","url":"https://github.com/chenxin-dlut/seqtrackv2"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48267,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"OSTrack","metrics":{"AUC":"50.6","Normalized Precision":"61.3","Precision":"57.6"},"paper_url":"https://arxiv.org/abs/2203.11991v4","paper_title":"Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework","paper_date":"2022-03-22","code_links":[{"title":"botaoye/ostrack","url":"https://github.com/botaoye/ostrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48268,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"KeepTrack","metrics":{"AUC":"48.2"},"paper_url":"https://arxiv.org/abs/2103.16556v2","paper_title":"Learning Target Candidate Association to Keep Track of What Not to Track","paper_date":"2021-03-30","code_links":[{"title":"visionml/pytracking","url":"https://github.com/visionml/pytracking"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":48269,"task":"Visual Object Tracking","parent_task":"Object Tracking","dataset":"LaSOT-ext","model_name":"LTMU","metrics":{"AUC":"41.4","Normalized Precision":"49.9","Precision":"47.3"},"paper_url":"https://arxiv.org/abs/2004.00305v1","paper_title":"High-Performance Long-Term Tracking with Meta-Updater","paper_date":"2020-04-01","code_links":[{"title":"Daikenan/LTMU","url":"https://github.com/Daikenan/LTMU"},{"title":"wangdongdut/Long-term-Visual-Tracking","url":"https://github.com/wangdongdut/Long-term-Visual-Tracking"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143681,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"SAMURAI-L","metrics":{"AUC":"61.0","Normalized Precision":"73.9","Precision":"72.2"},"paper_url":"https://arxiv.org/abs/2411.11922v2","paper_title":"SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory","paper_date":"2024-11-18","code_links":[{"title":"yangchris11/samurai","url":"https://github.com/yangchris11/samurai"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143682,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"DAM4SAM","metrics":{"AUC":"60.9"},"paper_url":"https://arxiv.org/abs/2411.17576v2","paper_title":"A Distractor-Aware Memory for Visual Object Tracking with SAM2","paper_date":"2024-11-26","code_links":[{"title":"jovanavidenovic/dam4sam","url":"https://github.com/jovanavidenovic/dam4sam"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143683,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"LoRAT-L-378","metrics":{"AUC":"56.6","Normalized Precision":"69.0","Precision":"65.1"},"paper_url":"https://arxiv.org/abs/2403.05231v2","paper_title":"Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance","paper_date":"2024-03-08","code_links":[{"title":"litinglin/lorat","url":"https://github.com/litinglin/lorat"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143684,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"LoRAT-g-378","metrics":{"AUC":"56.5","Normalized Precision":"69.0","Precision":"64.9"},"paper_url":"https://arxiv.org/abs/2403.05231v2","paper_title":"Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance","paper_date":"2024-03-08","code_links":[{"title":"litinglin/lorat","url":"https://github.com/litinglin/lorat"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143685,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"UNINEXT-H","metrics":{"AUC":"56.2","Normalized Precision":"63.8","Precision":"63.8"},"paper_url":"https://arxiv.org/abs/2303.06674v2","paper_title":"Universal Instance Perception as Object Discovery and Retrieval","paper_date":"2023-03-12","code_links":[{"title":"MasterBin-IIAU/UNINEXT","url":"https://github.com/MasterBin-IIAU/UNINEXT"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143686,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"MCITrack-L384","metrics":{"AUC":"55.7","Normalized Precision":"66.5","Precision":"62.9"},"paper_url":"https://arxiv.org/abs/2412.11023v1","paper_title":"Exploring Enhanced Contextual Information for Video-Level Object Tracking","paper_date":"2024-12-15","code_links":[{"title":"kangben258/MCITrack","url":"https://github.com/kangben258/MCITrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143687,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"RTracker-L","metrics":{"AUC":"54.9","Normalized Precision":"65.5","Precision":"62.7"},"paper_url":"https://arxiv.org/abs/2403.19242v1","paper_title":"RTracker: Recoverable Tracking via PN Tree Structured Memory","paper_date":"2024-03-28","code_links":[{"title":"norahgreen/rtracker","url":"https://github.com/norahgreen/rtracker"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143688,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"MCITrack-B224","metrics":{"AUC":"54.6","Normalized Precision":"65.7","Precision":"62.1"},"paper_url":"https://arxiv.org/abs/2412.11023v1","paper_title":"Exploring Enhanced Contextual Information for Video-Level Object Tracking","paper_date":"2024-12-15","code_links":[{"title":"kangben258/MCITrack","url":"https://github.com/kangben258/MCITrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143689,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"ODTrack-L","metrics":{"AUC":"53.9"},"paper_url":"https://arxiv.org/abs/2401.01686v1","paper_title":"ODTrack: Online Dense Temporal Token Learning for Visual Tracking","paper_date":"2024-01-03","code_links":[{"title":"gxnu-zhonglab/odtrack","url":"https://github.com/gxnu-zhonglab/odtrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143690,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"ARTrackV2-L","metrics":{"AUC":"53.4","Normalized Precision":"63.7","Precision":"60.2"},"paper_url":"https://arxiv.org/abs/2312.17133v3","paper_title":"ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe","paper_date":"2023-12-28","code_links":[{"title":"miv-xjtu/artrack","url":"https://github.com/miv-xjtu/artrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143691,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"HIPTrack","metrics":{"AUC":"53.0","Normalized Precision":"64.3","Precision":"60.6"},"paper_url":"https://arxiv.org/abs/2311.02072v2","paper_title":"HIPTrack: Visual Tracking with Historical Prompts","paper_date":"2023-11-03","code_links":[{"title":"wenruicai/hiptrack","url":"https://github.com/wenruicai/hiptrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143692,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"ARTrack-L","metrics":{"AUC":"52.8","Normalized Precision":"62.9","Precision":"59.7"},"paper_url":"http://openaccess.thecvf.com//content/CVPR2023/html/Wei_Autoregressive_Visual_Tracking_CVPR_2023_paper.html","paper_title":"Autoregressive Visual Tracking","paper_date":"2023-01-01","code_links":[{"title":"miv-xjtu/artrack","url":"https://github.com/miv-xjtu/artrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143693,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"DropTrack","metrics":{"AUC":"52.7","Precision":"60.2"},"paper_url":"https://arxiv.org/abs/2304.00571v2","paper_title":"DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks","paper_date":"2023-04-02","code_links":[{"title":"jimmy-dq/dropmae","url":"https://github.com/jimmy-dq/dropmae"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143694,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"ODTrack-B","metrics":{"AUC":"52.4"},"paper_url":"https://arxiv.org/abs/2401.01686v1","paper_title":"ODTrack: Online Dense Temporal Token Learning for Visual Tracking","paper_date":"2024-01-03","code_links":[{"title":"gxnu-zhonglab/odtrack","url":"https://github.com/gxnu-zhonglab/odtrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143695,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"SeqTrack-L384","metrics":{"AUC":"50.7","Normalized Precision":"61.6","Precision":"57.5"},"paper_url":"https://arxiv.org/abs/2304.14394v3","paper_title":"Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking","paper_date":"2023-04-27","code_links":[{"title":"chenxin-dlut/seqtrackv2","url":"https://github.com/chenxin-dlut/seqtrackv2"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143696,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"OSTrack","metrics":{"AUC":"50.6","Normalized Precision":"61.3","Precision":"57.6"},"paper_url":"https://arxiv.org/abs/2203.11991v4","paper_title":"Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework","paper_date":"2022-03-22","code_links":[{"title":"botaoye/ostrack","url":"https://github.com/botaoye/ostrack"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143697,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"KeepTrack","metrics":{"AUC":"48.2"},"paper_url":"https://arxiv.org/abs/2103.16556v2","paper_title":"Learning Target Candidate Association to Keep Track of What Not to Track","paper_date":"2021-03-30","code_links":[{"title":"visionml/pytracking","url":"https://github.com/visionml/pytracking"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":143698,"task":"Visual Object Tracking","parent_task":null,"dataset":"LaSOT-ext","model_name":"LTMU","metrics":{"AUC":"41.4","Normalized Precision":"49.9","Precision":"47.3"},"paper_url":"https://arxiv.org/abs/2004.00305v1","paper_title":"High-Performance Long-Term Tracking with Meta-Updater","paper_date":"2020-04-01","code_links":[{"title":"Daikenan/LTMU","url":"https://github.com/Daikenan/LTMU"},{"title":"wangdongdut/Long-term-Visual-Tracking","url":"https://github.com/wangdongdut/Long-term-Visual-Tracking"}],"metrics_order":"[\"AUC\", \"Normalized Precision\", \"Precision\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]}]}