Pedestrian-motorcycle binary classification using data augmentation and convolutional neural networks

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

Advances in Intelligent Systems and Computing

Volume

997

First Page

725

Last Page

737

Publication Date

1-1-2019

Abstract

One common problem in vehicle and pedestrian detection algorithms is the mis-classification of motorcycle riders as pedestrians. This paper focused on a binary classification technique using convolutional neural networks for pedestrian and motorcycle riders in different road context locations. The study also includes a data augmentation technique to address the un-balanced number of training images for a machine learning algorithm. This problem in un-balanced data sets usually cause a prediction bias, in which the prediction for a learned data set usually favors the class with more image representations. Using four data sets with differing road context (DS0, DS3-1, DS4-3, and DS4-3), the binary classification between pedestrian and motorcycle riders achieved good results. In DS0, training accuracy is 96.96% while validation accuracy is 81.52%. In DS3-1, training accuracy is 93.17% while validation accuracy is 86.58%. In DS4-1, training accuracy is 94.42% while validation accuracy is 97.00%. In DS4-3, training accuracy is 95.94% while validation accuracy is 88.59%. © 2019, Springer Nature Switzerland AG.

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Digitial Object Identifier (DOI)

10.1007/978-3-030-22871-2_50

Disciplines

Computer Sciences

Keywords

Neural networks (Computer science); Human activity recognition; Pedestrians; Image processing

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