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TransTrack: Multiple Object Tracking with Transformer

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arxiv 2012.15460 v2 pith:WWBEVTCR submitted 2020-12-31 cs.CV

Peize Sun , Jinkun Cao , Yi Jiang , Rufeng Zhang , Enze Xie , Zehuan Yuan , Changhu Wang , Ping Luo This is my paper · ORCID
classification cs.CV
keywords objecttranstrackmultipletrackingframemethodsnoveltransformer
verification ladder T0 review T1 audit T2 compute T3 formal
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In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set of learned object queries to enable detecting new-coming objects. It builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot, simplifying complicated multi-step settings in tracking-by-detection methods. On MOT17 and MOT20 benchmark, TransTrack achieves 74.5\% and 64.5\% MOTA, respectively, competitive to the state-of-the-art methods. We expect TransTrack to provide a novel perspective for multiple object tracking. The code is available at: \url{https://github.com/PeizeSun/TransTrack}.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The generator is the tracker: Multi-object tracking by painting persistent identity colours

    cs.CV 2026-07 conditional novelty 7.0 of 10

    A video generator fine-tuned to emit persistent per-person colors tracks DanceTrack dancers at 40.3 HOTA with no detector or tracker.

  2. CD-RMOT-Bench: Benchmarking the Cross-Domain Referring Multi-Object Tracking

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Cross-domain language-guided tracking degrades sharply under weather/viewpoint/realism shifts, and a query-centric adaptation method partially restores performance.

  3. COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Continuous TAO annotations plus multi-cue fusion, hierarchical aggregation, and temporal confidence propagation raise novel TETA to 35.4%/30.5% on TAO val/test.

  4. Generative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework

    cs.CV 2026-01 reject novelty 6.0 of 10

    A new LLM-generated dataset and an MLLM-based tracker claim state-of-the-art semantic multi-object tracking, but the evaluation protocol masks missed objects and ID switches.

  5. To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A taxonomy that classifies unified perception methods in autonomous driving into Early, Late, and Full Unified Perception based on task integration, tracking formulation, and representation flow.

  6. FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment

    cs.CV 2025-05 conditional novelty 6.0 of 10

    FusionTrack jointly optimizes single-view tracking and cross-view re-identification in a Transformer, and the new MDMOT benchmark covers arbitrary multi-drone views with overlapping and non-overlapping cameras.

  7. From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety

    cs.CV 2025-09 accept novelty 5.0 of 10

    A survey organizing recent camera-based AI methods for vulnerable road user safety into four interlocking visual tasks and four open deployment challenges.

  8. CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios

    cs.CV 2025-07 conditional novelty 5.0 of 10

    CrowdTrack is a dense, first-person-view pedestrian tracking benchmark that exposes large performance drops in existing multi-object trackers.

  9. Depth-Aware Scoring and Hierarchical Alignment for Multiple Object Tracking

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A training-free MOT framework that adds zero-shot depth histograms and a hierarchical box/mask alignment score to association, with mixed state-of-the-art results.

  10. A Deep Dive into Generic Object Tracking: A Survey

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A survey that categorizes generic object tracking into Siamese, discriminative, and transformer-based paradigms and compares them across architecture and performance.

  11. YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A YOLOv8 detector trained on overlapping slices plus an OC-SORT tracker with EMA motion direction and expanded IoU distance penalty achieves 55.205 SO-HOTA on the SMOT4SB public test set.

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