{"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/towards-grand-unification-of-object-tracking","title":"Towards Grand Unification of Object Tracking","arxiv_id":"2207.07078","date":"2022-07-14","proceeding":null,"authors":["Bin Yan","Yi Jiang","Peize Sun","Dong Wang","Zehuan Yuan","Ping Luo","Huchuan Lu"],"abstract":"We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. Due to the fragmented definitions of the object tracking problem itself, most existing trackers are developed to address a single or part of tasks and overspecialize on the characteristics of specific tasks. By contrast, Unicorn provides a unified solution, adopting the same input, backbone, embedding, and head across all tracking tasks. For the first time, we accomplish the great unification of the tracking network architecture and learning paradigm. Unicorn performs on-par or better than its task-specific counterparts in 8 tracking datasets, including LaSOT, TrackingNet, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. We believe that Unicorn will serve as a solid step towards the general vision model. Code is available at https://github.com/MasterBin-IIAU/Unicorn.","url_abs":"https://arxiv.org/abs/2207.07078v4","url_pdf":"https://arxiv.org/pdf/2207.07078v4.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":"towards-grand-unification-of-object-tracking","repo_url":"https://github.com/masterbin-iiau/unicorn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"multi-object-tracking-and-segmentation","task_name":"Multi-Object Tracking and Segmentation"},{"task_slug":"multiple-object-tracking","task_name":"Multiple Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"video-object-tracking","task_name":"Video Object Tracking"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"}],"methods":[{"method_slug":"vos","method_name":"VOS"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-mot17","task":"Multi-Object Tracking","dataset":"MOT17","model":"Unicorn","rank_in_archive_order":21,"of":48,"metrics":{"HOTA":"61.7","IDF1":"75.5","MOTA":"77.2"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-on-mots20","task":"Multi-Object Tracking","dataset":"MOTS20","model":"Unicorn","rank_in_archive_order":3,"of":6,"metrics":{"IDF1":"65.9","sMOTSA":"65.3"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-and-segmentation-on-3","task":"Multi-Object Tracking and Segmentation","dataset":"BDD100K val","model":"Unicorn","rank_in_archive_order":2,"of":8,"metrics":{"mMOTSA":"29.6"},"uses_additional_data":false},{"leaderboard":"/sota/multiple-object-tracking-on-bdd100k-val","task":"Multiple Object Tracking","dataset":"BDD100K val","model":"Unicorn","rank_in_archive_order":6,"of":9,"metrics":{"TETA":"-","mIDF1":"54.0","mMOTA":"41.2"},"uses_additional_data":false},{"leaderboard":"/sota/video-object-tracking-on-nv-vot211","task":"Video Object Tracking","dataset":"NT-VOT211","model":"Unicorn","rank_in_archive_order":23,"of":43,"metrics":{"AUC":"34.52","Precision":"47.77"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-lasot","task":"Visual Object Tracking","dataset":"LaSOT","model":"Unicorn","rank_in_archive_order":33,"of":46,"metrics":{"AUC":"68.5","Normalized Precision":"76.6","Precision":"74.1"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-trackingnet","task":"Visual Object Tracking","dataset":"TrackingNet","model":"Unicorn","rank_in_archive_order":24,"of":40,"metrics":{"Accuracy":"83","Normalized Precision":"86.4","Precision":"82.2"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2207.07078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07078"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/masterbin-iiau/unicorn","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/MasterBin-IIAU/Unicorn","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1,"ran_draft_wrong":2,"ran_honours":2,"ran_violates":1},"by_repo_kind":{"official":{"samples":5,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"656fcd317891ebbb","entry":"Unicorn","repo":"MasterBin-IIAU/Unicorn","repo_kind":"official","path":"unicorn/models/unicorn.py","file_url":"https://github.com/MasterBin-IIAU/Unicorn/blob/HEAD/unicorn/models/unicorn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"656fcd317891ebbb"}},{"code_sha256_prefix":"69773d3745bb24e2","entry":"average_dict","repo":"MasterBin-IIAU/Unicorn","repo_kind":"official","path":"unicorn/models/unicorn.py","file_url":"https://github.com/MasterBin-IIAU/Unicorn/blob/HEAD/unicorn/models/unicorn.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"69773d3745bb24e2"}},{"code_sha256_prefix":"e0a06ded5d4f6c3c","entry":"box_cxcywh_to_xyxy","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"e0a06ded5d4f6c3c"}},{"code_sha256_prefix":"a93d04d5a5f05f9d","entry":"compute_corr_losses","repo":"MasterBin-IIAU/Unicorn","repo_kind":"official","path":"unicorn/models/unicorn.py","file_url":"https://github.com/MasterBin-IIAU/Unicorn/blob/HEAD/unicorn/models/unicorn.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a93d04d5a5f05f9d"}},{"code_sha256_prefix":"ac6d50c75c2fe223","entry":"dice_coefficient","repo":"MasterBin-IIAU/Unicorn","repo_kind":"official","path":"unicorn/models/unicorn.py","file_url":"https://github.com/MasterBin-IIAU/Unicorn/blob/HEAD/unicorn/models/unicorn.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ac6d50c75c2fe223"}},{"code_sha256_prefix":"65c1d3839e2d728a","entry":"get_label_map","repo":"MasterBin-IIAU/Unicorn","repo_kind":"official","path":"unicorn/models/unicorn.py","file_url":"https://github.com/MasterBin-IIAU/Unicorn/blob/HEAD/unicorn/models/unicorn.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"65c1d3839e2d728a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}