1. Machine Learning for Pedestrian Detection

2. How does a Smart Assistance System detects Pedestrian?

3. Phases Object Segmentation Feature Extraction Classification Get foreground image and segment Extract the relevant features in the image Classifies the images into respective classes

4. Feature Extraction  Using HAAR Transform  Each Rectangle Bar represents a Feature  subtraction of sum of rectangle grey scale of black block and white block gives the intensity of the pixel

5. Classification with Adaboost and SVM

6. Support Vector machine margin Others SVM

7. Training Data Positive Samples Negative Samples

8. Analysis IS Pedestrian?Predicted : YesPredicted : No Total Positive Samples: P True Positive : TPFalse Negative Total Negative Samples: N False Positive : FP True Negative: TN Accuracy (AC): (TP+TN)/(P+N) Detection Rate (DR): TP/P False Alarm Rate : FP/N

9. Comparison of Results Classifier Data Sets(P=100,N=500) 1 2 Single SVM AR(%) 99.90 99.43 DR(%) 99.40 96.60 FPR(%) 0.00 Cascade- Adaboost-SVM AR(%) 99.90 99.53 DR(%) 99.40 97.20 FPR(%) 0.00

10. Classifier Comparison Data Set Cascade Classifier SVM Number of SV’s 1 423 2400 2 646 2400  Comparison of number of support vectors between cascade classifier and SVM

11. Let’s watch  https://www.youtube.com/watch?v=uX5wGeYwrz0&list=PLMbfssWJTEMny EmlaEA767A5K-hduKQRx&index=14 Volvo – S60 Pedestrian Detection System

12. Other Applications  Surveillance Systems  Starts Recording after detecting the Pedestrians.  Reduce the space to store the videos.

13. Contd..  Human Robot Interactions

14. Any Queries ??