1. Using Statistic-based Boosting Cascade Weilong Yang, Wei Song, Zhigang Qiao, Michael Fang 1

2.  Haar-like feature generation ◦ Integral image  AdaBoost (feature selection) ◦ Limitations  Statistic-based boosting (StatBoost)  Cascade structure  Performance evaluation 2

3.  The objective is to learn a sequence of best weak classifiers and the best combining weights  Each weak classifier is constructed based on one feature (1D). 3

4. Weak classifier 3 Final classifier: linear combination of the weak classifiers Weights Increased Weak Classifier 2 Weak Classifier 1 4

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6. It takes WEEKS to train! 6

7. Training of the weak classifier 7 Figure courtesy to Minh-Tri Phan, 2007

8.  Train the weak classifiers using statistics  Assume the feature values of each class are of normal distribution Non-car Car Optimal threshold Feature value 8 Figure courtesy to Minh-Tri Phan, 2007

9. Constant Time 9 Figure courtesy to Minh-Tri Phan, 2007

10.  Motivation ◦ Increase detection performance ◦ Reduce computation time  Key insight ◦ Use simpler classifiers to reject the majority of sub- windows ◦ Use more complex ones to achieve low false positive rates on the rest of them 10

11.  Overall form: degenerate decision tree  Reflects the fact: within any single image an overwhelming majority of sub-windows are negative [1] 11 All Sub-windows 123 Rejected Sub-windows Further Processing T TT F FF

12.  False positive rate  Detection rate 12

13.  Three-layer cascade  Each layer has 4, 11, and 73 weak classifiers, respectively 13

14. Number of car images: 500 14 (Courtesy to UIUC Image Database for Car Detection)

15. Number of non-car images: 500 15 (Courtesy to UIUC Image Database for Car Detection)

16. 1 st Feature2 nd Feature3 rd Feature 16

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21. Test results on 170 Images: 21

22. 1. P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” In Proc. CVPR, 2001. 2. M. Pham, T. Cham, “Fast training and selection of Haar features using statistics in boosting-based face detection.” In Proc. ICCV 2007 22