PowerPoint Presentation

Machine Learning forPedestrian Detection1How does a Smart Assistance System detects Pedestrian?

2Phases Object SegmentationFeature ExtractionClassificationGet foreground image and segment Extract the relevant features in the imageClassifies the images into respective classes3Feature ExtractionUsing HAAR Transform

Each Rectangle Bar represents a Featuresubtraction of sum of rectangle grey scale of black block and white block gives the intensity of the pixel

4Classification with Adaboost and SVM

Support Vector machinemarginOthersSVM6Training Data

Positive SamplesNegative Samples7AnalysisIS Pedestrian?Predicted : YesPredicted : NoTotal Positive Samples: PTrue Positive : TPFalse NegativeTotal Negative Samples: NFalse Positive : FPTrue Negative: TNAccuracy (AC): (TP+TN)/(P+N)

Detection Rate (DR): TP/P

False Alarm Rate : FP/N8Comparison 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 0.00 Cascade-Adaboost-SVM AR(%) 99.90 99.53 DR(%) 99.40 97.20 FPR(%) 0.00 0.00Classifier Comparison

Data SetCascade Classifier SVMNumber of SVs Number of SVs 1 423 2400 2 646 2400Comparison of number of support vectors between cascade classifier and SVMLets watch https://www.youtube.com/watch?v=uX5wGeYwrz0&list=PLMbfssWJTEMnyEmlaEA767A5K-hduKQRx&index=14Volvo S60 Pedestrian Detection SystemOther ApplicationsSurveillance SystemsStarts Recording after detecting the Pedestrians.Reduce the space to store the videos.

Contd..Human Robot Interactions

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