Browse State-of-the-Art › Multi-Object Tracking
Multi-Object Tracking
279 papers with code · 22 benchmarks · 43 datasets archive 2025-07-28
Multi-Object Tracking is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
22 leaderboard tables shown for this task, 22 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 22 until expanded.
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
43 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 43 until expanded.
Subtasks archive 2025-07-28
6 subtasks in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
30 shown of 279 papers with code (671 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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2 Feb 2016 56 repositories listed Syntology ran 15 of 40 samples · 25 unverified · 7 pointer-only (licence)This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications.
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4 Apr 2020 33 repositories listed Syntology ran 8 of 53 samples · 45 unverifiedFormulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
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28 Feb 2022 14 repositories listed Syntology ran 3 of 20 samples · 17 unverified · 2 pointer-only (licence)As a result, the construction of a good baseline for a fair comparison is essential.
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13 Mar 2019 13 repositories listed Syntology ran 3 of 14 samples · 11 unverified · 2 pointer-only (licence)Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions.
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27 Sep 2019 12 repositories listed Syntology ran 2 of 36 samples · 34 unverifiedIn this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
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13 Oct 2021 10 repositories listed Syntology ran 1 of 12 samples · 11 unverifiedByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
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30 Mar 2022 8 repositories listed Syntology ran 5 of 27 samples · 22 unverifiedIn this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
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2 Mar 2016 8 repositories listed Syntology ran 11 of 38 samples · 27 unverified · 1 pointer-only (licence)Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking…
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29 Jun 2022 7 repositories listed Syntology ran 4 of 10 samples · 6 unverified · 1 pointer-only (licence)The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object.
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27 Mar 2022 7 repositories listed Syntology ran 15 of 31 samples · 16 unverified · 2 pointer-only (licence)Instead of relying only on the linear state estimate (i.
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2 Apr 2020 7 repositories listed Syntology ran 4 of 16 samples · 12 unverifiedNowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.
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8 Mar 2022 6 repositories listedJoint detection and embedding (JDE) based methods usually estimate bounding boxes and embedding features of objects with a single network in Multi-Object Tracking (MOT).
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17 Nov 2021 5 repositories listedMulti-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers.
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16 Sep 2020 5 repositories listed Syntology ran 2 of 10 samples · 8 unverifiedMulti-Object Tracking (MOT) has been notoriously difficult to evaluate.
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17 Nov 2022 4 repositories listedIn this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
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5 Jan 2022 4 repositories listedIn the second approach, although DeepSORT only processes a quarter of all frames due to hardware and time limitations, our model with DeepSORT (42.
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24 Mar 2021 4 repositories listedMoreover, we also propose a new head detector, HeadHunter, which is designed for small head detection in crowded scenes.
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10 Mar 2021 4 repositories listedThe code and protocols for our benchmark and algorithm are available at https://github.
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18 Jan 2021 4 repositories listedThe decoder part is inspired by the Cascaded Partial Decoder, known for fast and accurate salient object detection.
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23 Oct 2020 4 repositories listed Syntology ran 2 of 15 samples · 13 unverifiedHowever, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm.
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6 Nov 2018 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points.
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12 May 2018 4 repositories listed Syntology ran 0 of 2 samples · 2 unverifiedDatasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
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28 May 2024 3 repositories listedSpecifically, we exploit the 2D detections and extracted features from multiple cameras to provide a better approximation of the multi-object filtering density to realize the track initiation/termination and…
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23 Feb 2023 3 repositories listed Syntology ran 2 of 10 samples · 8 unverifiedMotion-based association for Multi-Object Tracking (MOT) has recently re-achieved prominence with the rise of powerful object detectors.
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27 Oct 2022 3 repositories listedWe present our 1st place solution to the Group Dance Multiple People Tracking Challenge.
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29 Nov 2021 3 repositories listedA typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association.
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23 Aug 2021 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles.
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14 Jul 2021 3 repositories listedIn terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0.
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29 Apr 2021 3 repositories listed Syntology ran 0 of 4 samples · 4 unverifiedMulti-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time.
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11 Jun 2020 3 repositories listedCompared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.
Syntology lines on 18 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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