Open AccessClassification of Pedestrian using Convoluted Neural Network
Author(s) -
Rohini. A. Chavan,
Sachin. R. Gengaje,
Shilpa. P. Gaikwad
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i7783.078919
Subject(s) - computer science , artificial intelligence , convolutional neural network , pedestrian detection , feature extraction , pattern recognition (psychology) , set (abstract data type) , pedestrian , feature (linguistics) , sliding window protocol , artificial neural network , window (computing) , engineering , linguistics , philosophy , transport engineering , programming language , operating system
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.