Tutorials/Object Tracking and Multi-Object Tracking/Tracking Evaluation and Intelligent Video Analytics
TR-04Advanced / Research 3 Hours
Tracking Evaluation and Intelligent Video Analytics
Evaluate tracking systems using detection, identity, association, trajectory, and runtime metrics. Students conduct controlled tracker comparisons and transform tracked trajectories into practical video-analytics applications.
Pythonpy-motmetricsTrackEvalPandasOpenCVMatplotlib
Code Structure Note
These notes are provided only to help students understand the code structure. Students should review, optimize, and adapt the code before using it in assignments, projects, or experiments.
Topics Covered
- 1Ground-truth trajectories
- 2False positives and false negatives
- 3Identity switches and fragmentation
- 4Mostly tracked and mostly lost
- 5MOTA, MOTP, IDF1, and HOTA
- 6Detection and association accuracy
- 7Runtime, FPS, latency, and memory
- 8Trajectory and event analytics
Learning Objectives
- Interpret MOTA, MOTP, IDF1, HOTA, FPS, and latency
- Design a controlled comparison with fixed detector and input settings
- Diagnose identity switches, fragmentation, and localization errors
- Build counting, trajectory, dwell-time, and speed-analysis applications
Key Takeaways
- No single metric captures detection, identity, localization, and runtime quality
- A controlled experiment keeps the YOLO26 detector and evaluation conditions fixed
- Physical speed requires calibration or a valid world-coordinate mapping
- Tracker state lives at runtime and is not stored permanently in a detector checkpoint
Hands-On Exercises
- 1Mark false positives, false negatives, and identity switches on a short sequence
- 2Compute a small MOTA and IDF1 example by hand
- 3Build a line-crossing counter with duplicate-event prevention
- 4Write a research-style tracker recommendation grounded in metrics and scene conditions
Tools & Libraries
Pythonpy-motmetricsTrackEvalPandasOpenCVMatplotlib