1. Related Work in Camera Network Tracking
Chih-Wei Wu

2. Outline Research Plan Paper Research Recording New Dataset
Media IC & System Lab
Chih-Wei Wu

3. A Camera Network Tracking (CamNeT) Dataset and Performance Baseline
A dataset from a camera network
How to benchmark camera network tracking
Address camera network tracking problem with social grouping model
Zhang, Shu, et al. "A camera network tracking (CamNeT) dataset and performance baseline." 2015 IEEE Winter Conference on Applications of Computer Vision. IEEE, 2015.
Media IC & System Lab
Chih-Wei Wu

4. A Camera Network Tracking (CamNeT) Dataset and Performance Baseline
A dataset from a camera network
6 scenarios
5-8 non-overlapping camera covering indoor & outdoor scene in university campus
Has people with predefine path within the camera network
Media IC & System Lab
Chih-Wei Wu

5. A Camera Network Tracking (CamNeT) Dataset and Performance Baseline
How to benchmark camera network tracking
Event-based
Tracking length (TL): Percentage of completed trajectory which was correctly tracked
Crossing fragments (XFrag): The number times that there is a link between two tracks within a specified tolerance, but missing in the tracking results
Crossing ID-switches (XIDS): The total number of times that there is no link between two tracks in two cameras within a specified tolerance of the ground truth trajectories, but one or more links exist in the tracking results.
Media IC & System Lab
Chih-Wei Wu

6. A Camera Network Tracking (CamNeT) Dataset and Performance Baseline
Address camera network tracking problem with social grouping model
Media IC & System Lab
Chih-Wei Wu

7. Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations
Use probability for ranking
Add 2 constraints when tracking
Uses mAP to evaluate multi-camera tracking
Report accuracy decrease after using what I so called “time model”
Huang, Wenxin, et al. "Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations." International Conference on Multimedia Modeling. Springer International Publishing, 2016.
Media IC & System Lab
Chih-Wei Wu

8. Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations
Use probability for ranking
𝑥 𝑂 𝑖 : probe image from camera O
𝑥 𝑚 𝑗 : image from camera m in the gallery
𝑡 𝑂 𝑖 : the time person i walks into view of probe camera O
𝑡 𝑚 𝑗 : the time person j walks into view of camera m
Media IC & System Lab
Chih-Wei Wu

9. Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations
Add 2 constraints when tracking
Temporal-spatial constraint:
A person would not appear in 2 camera at the same time: hinge loss
Use Weibull Distribution to model temporal-spatial probability
Multiple camera relation:
Media IC & System Lab
Chih-Wei Wu

10. Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations
Uses mAP to evaluate multi-camera tracking
Comment: mAP is still a image-to gallery method. Is it a good benchmark for multi-camera tracking?
Media IC & System Lab
Chih-Wei Wu

11. Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations
Report accuracy decrease after using what I so called “time model”
Temporal-spatial constraint
Multiple Camera Relation
AP_feature: use only visual feature (LOMO)
AP_joint: plus probability model
AP_global: plus 2 constraints mention above
Media IC & System Lab

12. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New benchmark for Camera Network Tracking
New dataset
A baseline tracking system
Ristani, Ergys, et al. "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking." arXiv preprint arXiv: (2016). Submited for CVPR 2016 ??
Media IC & System Lab
Chih-Wei Wu

13. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New benchmark for Camera Network Tracking
Traditional tracking use CLEAR MOT as evaluating measure (event-based)
Issues
Scenario 1: within camera issue
Media IC & System Lab
Chih-Wei Wu

14. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New benchmark for Camera Network Tracking
Traditional tracking use CLEAR MOT as evaluating measure (event-based)
Issues
Scenario 2: handover issue
Media IC & System Lab
Chih-Wei Wu

15. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New benchmark for Camera Network Tracking
Construct a bipartite graph 𝐺=( 𝑉 𝑇 , 𝑉 𝐶 ,𝐸)
𝑉 𝑇 : ground truth trajectory false positive trajectory(same as computed trajectory)
𝑉 𝐶 : computed trajectory false negative trajectory(same as ground truth trajectory)
Media IC & System Lab
Chih-Wei Wu

16. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New benchmark for Camera Network Tracking
Construct a bipartite graph 𝐺=( 𝑉 𝑇 , 𝑉 𝐶 ,𝐸)
𝑉 𝑇 : ground truth trajectory false positive trajectory(same as computed trajectory)
𝑉 𝐶 : computed trajectory false negative trajectory(same as ground truth trajectory)
edges: if trajectory overlaps in time
ground truth trajectory---- computed trajectory
ground truth trajectory----false negative trajectory
computed trajectory----false positive trajectory
Media IC & System Lab
Chih-Wei Wu

17. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New benchmark for Camera Network Tracking
Cost of an edge
Frame by frame miss
Using ground truth trajectory as baseline
Frame by frame miss
Using computed trajectory as baseline
*frame by frame miss: if spatial overlap doesn’t exceed Δ*union area
*cost for an non-ground-truth-computed edge: misses for the length of the trajectory
Media IC & System Lab
Chih-Wei Wu

18. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New benchmark for Camera Network Tracking
After bipartite matching by minimizimg edge cost
AT: all matched ground truth trajectory
AC: all matched computed trajectory
Media IC & System Lab
Chih-Wei Wu

19. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New benchmark for Camera Network Tracking
ID Precision:
𝐼𝐷𝑇𝑃 𝐼𝐷𝑇𝑃+𝐼𝐷𝐹𝑃
ID Recall:
𝐼𝐷𝑇𝑃 𝐼𝐷𝑇𝑃+𝐼𝐷𝐹𝑁
ID F1 score:
2𝐼𝐷𝑇𝑃 2𝐼𝐷𝑇𝑃+𝐼𝐷𝐹𝑃+𝐼𝐷𝐹𝑁
Media IC & System Lab
Chih-Wei Wu

20. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
New dataset
Labeled with 2D bounding box and 3D coordinate
Media IC & System Lab
Chih-Wei Wu

21. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
A baseline tracking system
Feature using HSV histogram
Tracking use Binary Integer Program(BIP)
Comment: Can this system work online or even real-time?
Media IC & System Lab
Chih-Wei Wu

22. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
A baseline tracking system
Media IC & System Lab
Chih-Wei Wu

23. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
A baseline tracking system
Media IC & System Lab
Chih-Wei Wu

24. Comment Key point for benchmarking camera network tracking:
Event-based v.s. tracking length based
Maybe a hierarchy evaluation would be a better solution for representing error
Intra-camera tracking error
Inter-camera tracking error
Media IC & System Lab
Chih-Wei Wu