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In this paper, we propose to handle unreliable detection by collecting\ncandidates from outputs of both detection and tracking. The intuition behind\ngenerating redundant candidates is that detection and tracks can complement\neach other in different scenarios. Detection results of high confidence prevent\ntracking drifts in the long term, and predictions of tracks can handle noisy\ndetection caused by occlusion. In order to apply optimal selection from a\nconsiderable amount of candidates in real-time, we present a novel scoring\nfunction based on a fully convolutional neural network, that shares most\ncomputations on the entire image. Moreover, we adopt a deeply learned\nappearance representation, which is trained on large-scale person\nre-identification datasets, to improve the identification ability of our\ntracker. Extensive experiments show that our tracker achieves real-time and\nstate-of-the-art performance on a widely used people tracking benchmark.","url_abs":"http://arxiv.org/abs/1809.04427v1","url_pdf":"http://arxiv.org/pdf/1809.04427v1.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":"real-time-multiple-people-tracking-with","repo_url":"https://github.com/longcw/MOTDT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"real-time-multiple-people-tracking-with","repo_url":"https://github.com/youngbin-ro/Homography-based-MOTDT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"real-time-multiple-people-tracking-with","repo_url":"https://github.com/youngbin-ro/MOTDT-original","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"large-scale-person-re-identification","task_name":"Large-Scale Person Re-Identification"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"multiple-people-tracking","task_name":"Multiple People Tracking"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"online-multi-object-tracking","task_name":"Online Multi-Object Tracking"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-mot16","task":"Multi-Object Tracking","dataset":"MOT16","model":"MOTDT","rank_in_archive_order":19,"of":24,"metrics":{"MOTA":"50.9"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-on-mot17","task":"Multi-Object Tracking","dataset":"MOT17","model":"MOTDT17","rank_in_archive_order":48,"of":48,"metrics":{"MOTA":"50.9"},"uses_additional_data":false},{"leaderboard":"/sota/online-multi-object-tracking-on-mot16","task":"Online Multi-Object Tracking","dataset":"MOT16","model":"MOTDT","rank_in_archive_order":4,"of":5,"metrics":{"MOTA":"47.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.04427","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.04427"}},"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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