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

• Main Authors: Billones, Robert Kerwin C., Bandala, Argel A., Gan Lim, Laurence A., Sybingco, Edwin, Fillone, Alexis M., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2019
• Subjects: Neural networks (Computer science); Human activity recognition; Pedestrians; Image processing; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2203; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3202/type/native/viewcontent
• Institution: De La Salle University

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 s...