Module 9: Multi-Camera Multi-Object Tracking
Advanced tracking with person detection and re-identification across multiple cameras.
Topics Covered
- OpenCV tracking API
- YOLOv4-tiny person detection
- Person re-identification (Re-ID)
- Multi-object tracking (MOT)
- Cross-camera tracking (MCMOT)
Tutorial Files
| File |
Description |
01_tracking_basics.py |
OpenCV tracking API, single object trackers |
02_person_detection.py |
YOLOv4-tiny person detection |
03_person_reid.py |
Person re-identification with deep features |
04_mot_tracker.py |
Multi-object tracking with SORT/DeepSORT concepts |
05_mcmot_multicam.py |
Cross-camera tracking and Re-ID matching |
Key Concepts
Tracking Pipeline
Detection → Feature Extraction → Association → Track Management
Re-ID Matching
- Extract appearance features using deep networks
- Compute cosine similarity between feature vectors
- Match across cameras using appearance + spatial cues
Key Functions Reference
| Function |
Description |
cv2.TrackerCSRT_create() |
Create CSRT tracker |
cv2.TrackerKCF_create() |
Create KCF tracker |
cv2.dnn.readNet() |
Load YOLO/Re-ID models |
cv2.dnn.blobFromImage() |
Prepare input for DNN |
tracker.init() |
Initialize tracker |
tracker.update() |
Update tracker position |
Further Reading