Classification of Pedestrian Using Convoluted Neural Network
Rohini. A. Chavan1, Sachin. R. Gengaje2, Shilpa. P. Gaikwad3
1Rohini. A. Chavan, Research Scholar, Department of Electronics, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.
2Sachin. R. Gengaje, Department of Electronics, Walchand Institute of Technology, Solapur, India.
3Shilpa. P. Gaikwad, Department of Electronics, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.
Manuscript received on 31 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1071-1075 | Volume-8 Issue-9, July 2019 | Retrieval Number: I7783078919/19©BEIESP | DOI: 10.35940/ijitee.I7783.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Classification, Detection, Convoluted Neural Network, Sliding Window.
Scope of the Article: Classification