Data checked 2026-08-13. Data may be stale - beyond the review cycle.
Review cycles are documented on the Methodology page. Methodology
ifzhang/ByteTrack
Algorithm Tier A perception_ai MIT
[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Engineering Snapshot
Use cases & tasks 5
- Best suited for
-
- perception engineers
- real-time video analytics
- pytorch-based pipelines
- Primary tasks
-
- multi-object tracking in video streams
- real-time object tracking
Stack & ecosystem
- Resource type
- Algorithm
- Ecosystem
- deployment · multi-object-tracking · pytorch · real-time
License & compliance
- License
-
MIT(Inferred)
Lifecycle & freshness Show
- Maintenance
- Active
- Latest version
- Not recorded
- Last activity
- 2026-08-13
- Last checked
- 2026-08-13
- Verification
- Official confirmed
What It Solves
Multi-object tracking in video by associating every detection box, including low-confidence ones, to maintain track identities.
Primary use cases
- multi-object tracking in video streams
- real-time object tracking
Secondary use cases
- integrating with detection models for tracking
When to Use
Consider when
- low-confidence detections are informative
- real-time performance required
- pytorch ecosystem used
Verify before adopting
- license is inferred MIT, confirm in repo
- specific detector compatibility unverified
- hardware targets not listed
Start Here
Adoption Checklist
- Needs verification license is inferred MIT, confirm in repo
- Needs verification specific detector compatibility unverified
- Needs verification hardware targets not listed
Each check stays "needs verification" until an official source confirms it; unconfirmed items are never marked verified.
Known Limitations & Unknowns
Known limitations
- no explicit hardware targets
- license status inferred
Not publicly verified
- supported detection frameworks
- UAV-specific performance
- deployment footprints
Alternatives & Related Tools
Alternatives
- iou-tracker — Alternative to
Related tools
How is it used?
Start from the recorded entry points below, then validate against the technical checklist.
- Official site https://github.com/FoundationVision/ByteTrack
- Repository https://github.com/ifzhang/ByteTrack
- Documentation https://github.com/FoundationVision/ByteTrack
Technical checklist
- OK License identified Recorded: MIT
- OK Maintenance signal Active
- OK Verification status Official confirmed
- OK Source evidence attached 1 source record(s)
- NEEDS REVIEW Latest version recorded Not recorded
Official Links
Metadata & Governance
| License | MIT — Inferred |
|---|---|
| Commercial model | Unknown |
| Maintenance status | Active |
| Verification status | Official confirmed — Confirmed via the official repository API responses in SourceRefs below. |
| Latest version | Not recorded |
| Latest release | Not recorded |
| Last activity | 2026-08-13 |
| Last checked | 2026-08-13 |
| First seen | Not recorded |
Dataset facts
Facts above come from the official dataset card only; unconfirmed fields stay unknown.
Related Resources & Dependencies
- yolox — depends on (verified)
- iou-tracker — alternative to (verified)
- tensorrt — compatible with (verified)
Recent Activity
New resource: ifzhang/ByteTrack
New repository resource added by the sprint promote pipeline.
Related Knowledge
Collections
- Aerial Perception Models and Datasets — Source-backed aerial dataset and model repositories for detection and tracking research and prototyping.
- Visual Perception Models and Data — Indexed model and dataset repositories for aerial visual perception research and prototyping.