Machine Learning for Pedestrian Detection

How does a Smart Assistance System detects Pedestrian?

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

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

Classification with Adaboost and SVM

Support Vector machine margin Others SVM

Training Data Positive Samples Negative Samples

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

Comparison of Results Classifier Data Sets(P=100,N=500) 1 2 Single SVM AR(%) DR(%) FPR(%) 0.00 Cascade- Adaboost-SVM AR(%) DR(%) FPR(%) 0.00

Classifier Comparison Data Set Cascade Classifier SVM Number of SV’s  Comparison of number of support vectors between cascade classifier and SVM

Let’s watch  EmlaEA767A5K-hduKQRx&index=14 Volvo – S60 Pedestrian Detection System

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

Contd..  Human Robot Interactions

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