mlsanityData to 14 Oct 2026

Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey

arXiv 2205.10766 ↗First posted 22 May 2022cs.CVcited by 8

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Abstract (arXiv)

Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories. With the advancement of deep neural networks and the increasing demand for intelligent video analysis, MOT has gained significantly increased interest in the computer vision community. Embedding methods play an essential role in object location estimation and temporal identity association in MOT. Unlike other computer vision tasks, such as image classification, object detection, re-identification, and segmentation, embedding methods in MOT have large variations, and they have never been systematically analyzed and summarized. In this survey, we first conduct a comprehensive overview with in-depth analysis for embedding methods in MOT from seven different perspectives, including patch-level embedding, single-frame embedding, cross-frame joint embedding, correlation embedding, sequential embedding, tracklet embedding, and cross-track relational embedding. We further summarize the existing widely used MOT datasets and analyze the advantages of existing state-of-the-art methods according to their embedding strategies. Finally, some critical yet under-investigated areas and future research directions are discussed.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 219 of its 338 references that are in the map. First posted on arXiv 22 May 2022.

Builds on

219 of 338 references.

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50 most cited of 219 All 219 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 2; 2026: 1.

8 papers.

  1. 2023DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenescited by 17
  2. 2022Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detectioncited by 5
  3. 2025Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Artcited by 4
  4. 2023Deep Learning Techniques for Video Instance Segmentation: A Surveycited by 3
  5. 2025CropTrack: A Tracking with Re-Identification Framework for Precision Agriculturecited by 1
  6. 2023S3Track: Self-supervised Tracking with Soft Assignment Flowcited by 0
  7. 2024The Progression of Transformers from Language to Vision to MOT: A Literature Review on Multi-Object Tracking with Transformerscited by 0
  8. 2026Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOTcited by 0