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The lack of large scale training and test data for this task has limited the community's ability to understand the performance of their tracking systems on a wide range of scenarios and conditions such as variations in person density, actions being performed, weather, and time of day. \\texttt{PersonPath22} dataset was specifically sourced to provide a wide variety of these conditions and our annotations include rich meta-data such that the performance of a tracker can be evaluated along these different dimensions. The lack of training data has also limited the ability to perform end-to-end training of tracking systems. As such, the highest performing tracking systems all rely on strong detectors trained on external image datasets. We hope that the release of this dataset will enable new lines of research that take advantage of large scale video based training data.","url_abs":"https://arxiv.org/abs/2211.02175v1","url_pdf":"https://arxiv.org/pdf/2211.02175v1.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":"large-scale-real-world-multi-person-tracking-1","repo_url":"https://github.com/amazon-research/tracking-dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object-tracking","task_name":"Object Tracking"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-personpath22","task":"Multi-Object Tracking","dataset":"PersonPath22","model":"ByteTrack","rank_in_archive_order":1,"of":4,"metrics":{"IDF1":"66.8","MOTA":"75.4"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-on-personpath22","task":"Multi-Object Tracking","dataset":"PersonPath22","model":"FairMOT","rank_in_archive_order":2,"of":4,"metrics":{"IDF1":"61.05","MOTA":"61.79"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-on-personpath22","task":"Multi-Object Tracking","dataset":"PersonPath22","model":"SiamMOT","rank_in_archive_order":3,"of":4,"metrics":{"IDF1":"53.71","MOTA":"67.52"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-on-personpath22","task":"Multi-Object Tracking","dataset":"PersonPath22","model":"CenterTrack","rank_in_archive_order":4,"of":4,"metrics":{"IDF1":"46.36","MOTA":"59.28"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.02175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.02175"}},"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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