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

Outline Introduction Proposed Approach Experimental Results Conclusion

Outline Introduction Proposed Approach Experimental Results Conclusion

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

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).

Outline Introduction Proposed Approach Experimental Results Conclusion

2. Proposed Approach A. Construction of the Multiscale Model

2. Proposed Approach A. Construction of the Multiscale Model

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

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

2. Proposed Approach B. Training Process for Learning Parameters

2. Proposed Approach C. Inference Process for Detecting Vehicles

2. Proposed Approach C. Inference Process for Detecting Vehicles

Outline Introduction Proposed Approach Experimental Results Conclusion

3. Experimental Results

3. Experimental Results

3. Experimental Results

3. Experimental Results

Outline Introduction Proposed Approach Experimental Results Conclusion

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.