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Selection of Algorithms for Pedestrian Detection During Day and Night

  • Rahul Pathak
  • P. SivrajEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 28)

Abstract

This paper presents an image processing based pedestrian detection system for day and night contributing to Advance Driver Assistance System (ADAS). The process, Histogram of Oriented Gradient (HOG) with Support Vector Machine (SVM) as linear classifier is compared and analyzed against Convolution Neural Network (CNN) for performance selection of best algorithm for pedestrian detection during both day and night. Performance analysis was done on standard datasets like INRIA, ETH, etc. and locally created datasets on Intel Processor with Ubuntu operating system. Implementation of HOG-SVM algorithm was performed, using DLib, python (2.7) and OpenCV (3.1.0) and accuracy of 96.25% for day and 96.55% for night was obtained. The implementation of Convolution Neural Network was performed using Anaconda3, TFLearn and python (3.6) and the scheme achieved an accuracy of 99.35% for day and 99.9% for night.

Keywords

ADAS CNN Histogram of oriented gradients Pedestrian detection Convolution neural networks 

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Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringAmrita School of EngineeringCoimbatoreIndia
  2. 2.Amrita Vishwa VidyapeethamCoimbatoreIndia
  3. 3.Department of Electrical and Electronics EngineeringAmrita School of EngineeringCoimbatoreIndia

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