Multi-Object Tracking and Segmentation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 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.

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

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

16 shown of 16 papers with code (28 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.

• BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
  12 May 2018 4 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
• EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
  29 Apr 2021 3 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time.
• D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
  15 Nov 2021 2 repositories listed
  We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.
• Universal Instance Perception as Object Discovery and Retrieval
  12 Mar 2023 1 repository listed Syntology ran 3 of 4 samples · 1 unverified
  All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple…
• Context-Aware Relative Object Queries To Unify Video Instance and Panoptic Segmentation
  1 Jan 2023 1 repository listed
  We evaluate the proposed approach across three challenging tasks: video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation.
• BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video
  25 Sep 2022 1 repository listed
  Multiple existing benchmarks involve tracking and segmenting objects in video e.
• Multi-Object Tracking and Segmentation via Neural Message Passing
  15 Jul 2022 1 repository listed
  We achieve state-of-the-art results for both tracking and segmentation in several publicly available datasets.
• Towards Grand Unification of Object Tracking
  14 Jul 2022 1 repository listed Syntology ran 6 of 6 samples · 0 unverified · 1 pointer-only (licence)
  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.
• PolyTrack: Tracking with Bounding Polygons
  2 Nov 2021 1 repository listed Syntology ran 3 of 4 samples · 1 unverified
  In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segmentation using bounding polygons.
• Do Different Tracking Tasks Require Different Appearance Models?
  5 Jul 2021 1 repository listed Syntology ran 6 of 17 samples · 11 unverified
  We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks…
• Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
  22 Jun 2021 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich spatio-temporal information for online multiple object tracking and segmentation.
• Continuous Copy-Paste for One-Stage Multi-Object Tracking and Segmentation
  1 Jan 2021 1 repository listed
  Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods.
• Assignment-Space-Based Multi-Object Tracking and Segmentation
  1 Jan 2021 1 repository listed
  In contrast, we formulate a global method for MOTS over the space of assignments rather than detections: First, we find all top-k assignments of objects detected and segmented between any two consecutive frames and…
• Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion
  31 Aug 2020 1 repository listed
  One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation…
• Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
  3 Jul 2020 1 repository listed
  The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5.
• PointTrack++ for Effective Online Multi-Object Tracking and Segmentation
  3 Jul 2020 1 repository listed
  In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.

Syntology lines on 7 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.