Classification of Pedestrian using Convoluted Neural Network

Abstract

We present our work based on classification of pedestrians into a single person and group of people using Convoluted Neural Network (CNN). Major work was done on classification-based feature extraction techniques before CNN is applied to it. CNN can classify objects without extracting the features. Here, we have set up a complete channel for pedestrian detection using sliding window approach and classification using a CNN network. Alex Net and ResNet are the two architectures used in CNN for implementing the classification algorithm. Performance is evaluated on the PET and Caltech dataset which consists of a number of people who are walking with a group or separately in the scene. We got the optimistic results in case of small dataset used for testing. We have also tested our algorithm over large dataset to verify its performance with the help of performance evaluation metrics.