Road Sign Recognition System Based on GentleBoost with Sharing Features

1. Road Sign Recognition System Based on GentleBoost with Sharing Features Jin-Yi Wu, Chien-Chung Tseng,Chun-Hao Chang, Jenn-Jier James Lien*, Ju Chin Chen, Ching Ting Tu ICSSE 2011

2. Outline • Goal • Method • Detection Module • Recognition Module • Experimental Result • Future work

3. Goal • Guide the driver to drive in the correct lane and at the right speed. • support the driver during the tedious task of remembering the large number of road signs.

4. Flowchart

5. Method: two modules • Detection Module • Stage 1:Color-Based, finding sign candidates. • Stage 2:Shap-Based, Classification. • Recognition Module • Stage 1: GenteBoost with Sharing Features • Stage 2: Rotation, scale, translation invariant

6. Detection ModuleStage 1: Color-Based Segmentation • Road signs are designed using colors to reflect it’s message. • These colors stand out from the environment.

7. HIS color space • hue saturation intensity (HSI) domain are sufficient to isolate road signs in a scene. [4] S. M. Bascon, S. L. Arroyo, P. G. Jimenez, H. G. Moreno, F. L. Ferreras, "Road-Sign Detection and Recognition Based on Support Vector Machines", IEEE Transaction Intelligent Transportation Systems, vol. 8, no. 2, pp. 264-278, 2007.

8. Threshold • the response to varying wavelength and intensity of standard imaging is nonlinear and interdependent. • The database GRAM and other image are used to train the suitable threshold.

9. Candidate selection • Each connected object is called a blob. • A candidate blob must laeger than 30x30. • aspect ratio is delimited between 1.9 and 1/1.9(suggested in [4]) [4] S. M. Bascon, S. L. Arroyo, P. G. Jimenez, H. G. Moreno, F. L. Ferreras, "Road-Sign Detection and Recognition Based on Support Vector Machines", IEEE Transaction Intelligent Transportation Systems, vol. 8, no. 2, pp. 264-278, 2007.

10. Detection ModuleStage 2: Shape-Based Classification • Then Distance to borders (DtBs) feature and linear Support Vector Machine (SVM) are used to classify the shape of the blobs as [4]. [4] S. M. Bascon, S. L. Arroyo, P. G. Jimenez, H. G. Moreno, F. L. Ferreras, "Road-Sign Detection and Recognition Based on Support Vector Machines", IEEE Transaction Intelligent Transportation Systems, vol. 8, no. 2, pp. 264-278, 2007.

11. linear SVM • Database GRAM and other image. • DtBs result. • Classify the blobs into a certain shape, i.e. circular, triangular, rectangular shapes.

12. Method: two modules Detection Module Stage 1:Color-Based, finding sign candidates. Stage 2:Shap-Based, Classification. Recognition Module Stage 1: GenteBoost with Sharing Features Stage 2: Rotation, scale, translation invariant

13. Recognition ModuleStage 1: GenteBoost with Sharing Features • Use weak classifiers to form a stronger classifier.

14. Road sign database • 30 x 30 pixel. • 108 road signs: • 48 red triangular signs • 36 red circular signs • 15 blue circular signs • 9 blue rectangular signs

15. type2 Type3,4,5 type1 Chromatic parts(1/2) • 20x20-pixel. • 5 types of red circular. • used for ensuring the existence of the road signs.

16. Chromatic parts(2/2) • if the chromatic part matches one of the types, we lower the threshold for the according type in RST-Invariant template matching due to the high probability that road sign in the same type may appear.

17. Rotation, scale, translation invariant(RST-invariant) • Red road signs: • Simply match the middle part of candidate blob(20x20-pixel). • The thresholds is adjusted by the result from the GentleBoost detector.(only red circular signs) • Blue road signs: • Simply match the complete candidate blob (30x30-pixel).

18. C(x, y)={C(x, y, r), r = 1 to R} Step 1: Circular sampling filter (Cifi) • R is the radius of the template. • Corr = correlation • Ti is ith templates with the same shape • If the Corr value is larger than a threshold tc, the template Ti is passed to second step, otherwise, Ti will be discard.

19. R(x,y) = R(x,y,α), α= 0 ~ 360} Step 2: Radial sampling filter (Rafi) • αis inclianation of Radial line, l is length of Radial line. • “cshiftj” means circular shifting j positions of theargument vector. • If Corr value is larger than a threshold tr, the template Tk willbe rotated with the corresponding angle and passed to the final step.

20. Corresponding with templatewhich pass the step2 ? There is no detail mention in this paper ? Step 3: template matching filter step

21. Thresholds • tc=0.9, tr=0.9, and tm=0.8 • tc=0.5, tr=0.5,and tm=0.45 for the corresponding type of the candidate blob.

22. Experimental result(1/2) • The detection rate and the false alarm rate for road signs in GRAM database, which is also used in [27] and [28], is 80.4% and 45.4, respectively. • 632 images for Experimental. [27] P. Gil-Jimenez, S. Lafuente-Arroyo, H. Gomez-Moreno, F. Lopez- Ferreras, and S. Maldonado-Bascon,” Traffic Sign Shape Classification Evaluation II : FFT Applied to The Sognature of Blobs,” in Proceedings of IEEE Intelligent Vehicles Symposium, pp. 607-612, 2005. [28] S. Lafuente-Arroyo, P. Gil-Jimenez, R. Maldonado-Bascon ,” Traffic Sign Shape Classification Evaluation I : SVM Using Distance to Broders,” in Proceedings of IEEE Intelligent Vehicles. Symposium, pp. 557-562, 2005.

23. Experimental result(2/2)

24. This work… • able to accurately classify different shapes of road signs in difficult conditions.(rotations, scaling, translations, and even partial occlusions.) • can run in almost real-time with 720x480-pixel image with average 12 fps on a 3.0-GHz CPU.

25. Future work to improvements • Same false alarm usually will not appear in adjacent frames. • Using different feature rather than DtBs in shape classification. • Extended to detect some other kinds of signboards such as signs of gas station or convenient shop