1. Author: Ye Li, Meng Joo Er, and Dayong Shen Speaker: Kai-Wen, Weng
A Novel Approach for Vehicle Detection Using an AND-OR Graph-Based Multiscale Model
Author: Ye Li, Meng Joo Er, and Dayong Shen
Speaker: Kai-Wen, Weng

2. Outline
Introduction
Proposed Approach
Experimental Results
Conclusion

3. Outline
Introduction
Proposed Approach
Experimental Results
Conclusion

4. 1. Introduction Object Detection Method: Color Based Machine Learning:
Adaboost (Adaptive Boost)
HoG (Histogram of Gradient)
SVM (Support Vector Machine)
AOG (AND-OR Graph)

5. 1. Introduction AND-OR Graph
Leaf-nodes: local classifiers for detecting contour fragments;
Or nodes: switches to activate one of its child leaf-nodes, making the model reconfigurable during inference;
And-nodes capture holistic shape deformations;
Root-node is also an or-node, which activates one of its child and-nodes to deal with large global variations (e.g. different poses and views).

6. Outline
Introduction
Proposed Approach
Experimental Results
Conclusion

7. 2. Proposed Approach
A. Construction of the Multiscale Model

8. 2. Proposed Approach
A. Construction of the Multiscale Model

9. 2. Proposed Approach AOG Model I : Image p : probability model
V : Vehicle instance detected
AV : AND Node
OV : OR Node
tV : terminal Node
EV : Edge

10. 2. Proposed Approach B. Training Process for Learning Parameters
q(I) is a reference distribution
Ku is the number of patches in
λuw is a coefficient of the th patch in
zuw is a normalization constant
r is a distance function measuring the similarity between the image region Iuw and the wth patch

11. 2. Proposed Approach
B. Training Process for Learning Parameters

12. 2. Proposed Approach
C. Inference Process for Detecting Vehicles

13. 2. Proposed Approach
C. Inference Process for Detecting Vehicles

14. Outline
Introduction
Proposed Approach
Experimental Results
Conclusion

15. 3. Experimental Results

16. 3. Experimental Results

17. 3. Experimental Results

18. 3. Experimental Results

19. Outline
Introduction
Proposed Approach
Experimental Results
Conclusion

20. 4. Conclusion
The use of global and local features and multiple appearances makes our model more suitable for describing multiscale vehicles in complex urban traffic conditions.