Data checked 2026-08-13. Data may be stale - beyond the review cycle.
Review cycles are documented on the Methodology page. Methodology
bochinski/iou-tracker
Software Tool Tier A perception_ai MIT
Python implementation of the IOU Tracker
Engineering Snapshot
Use cases & tasks 6
- Best suited for
-
- researchers needing a simple MOT baseline
- engineers prototyping detection+track pipelines in Python
- education/demo use (topic demo-script)
- Primary tasks
-
- Multiple object tracking from bounding-box detections
- Baseline tracker for MOT evaluation (topics: mot, detrac, ua-detrac)
- Python prototyping of tracking-by-detection
Stack & ecosystem
- Resource type
- Software Tool
- Ecosystem
- demo-script · detrac · evaluation · iou-tracker · mot · python
License & compliance
- License
-
MIT(Inferred)
Lifecycle & freshness Show
- Maintenance
- Active
- Latest version
- Not recorded
- Last activity
- 2026-07-08
- Last checked
- 2026-08-13
- Verification
- Official confirmed
What It Solves
Provide lightweight multiple object tracking (MOT) by associating per-frame detection boxes using intersection-over-union (tracking-by-detection).
Primary use cases
- Multiple object tracking from bounding-box detections
- Baseline tracker for MOT evaluation (topics: mot, detrac, ua-detrac)
- Python prototyping of tracking-by-detection
Secondary use cases
- Teaching IOU tracking concepts
- Evaluation scripting on DETRAC-style datasets
When to Use
Consider when
- A detector already outputs boxes per frame
- Re-identification after occlusion is not required
- Minimal compute is preferred
Verify before adopting
- License is inferred MIT from GitHub API – confirm LICENSE file
- No tagged release/version present
- Python version/platform support not specified
- No hardware targets listed
Start Here
Adoption Checklist
- Needs verification License is inferred MIT from GitHub API – confirm LICENSE file
- Needs verification No tagged release/version present
- Needs verification Python version/platform support not specified
- Needs verification No hardware targets listed
Each check stays "needs verification" until an official source confirms it; unconfirmed items are never marked verified.
Known Limitations & Unknowns
Known limitations
- No latest_version or release metadata
- License status inferred, not file-confirmed
- No platforms/hardware_targets specified
Not publicly verified
- Exact Python compatibility
- Benchmark on UAV-specific data
- Integration paths to flight stacks
Alternatives & Related Tools
Alternatives
- bytetrack — Alternative to
Related tools
- visdrone-dataset — Used for
How is it used?
Start from the recorded entry points below, then validate against the technical checklist.
- Official site http://www.nue.tu-berlin.de
- Repository https://github.com/bochinski/iou-tracker
- Documentation http://www.nue.tu-berlin.de
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 | 2026-07-08 |
| 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
- visdrone-dataset — used for (verified)
- bytetrack — alternative to (verified)
Recent Activity
New resource: bochinski/iou-tracker
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.