Real Time Person Detection and Classification using YOLO
Tejas Rao C1, Mohammed Zainuddin2, Shrishail M Patil3, Shashank G4, Nimrita Koul5
1Tejas Rao C, CIT, REVA University, Bangalore (Karnataka), India.
2Mohammed Zainuddin, CIT, REVA University, Bangalore (Karnataka), India.
3Shrishail M Patil, CIT, REVA University, Bangalore (Karnataka), India.
4Shashank G, CIT, REVA University, Bangalore (Karnataka), India.
5Nimrita Koul, CIT, REVA University, Bangalore (Karnataka), India.
Manuscript received on 04 June 2019 | Revised Manuscript received on 12 June 2019 | Manuscript Published on 29 June 2019 | PP: 36-39 | Volume-8 Issue-5S, May 2019 | Retrieval Number: E10080585S19/19©BEIESP
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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: A Convolutional Neural Network (CNN) is a class of deep neural network most commonly used in analyzing visual images. Various systems and applications have been built to detect and classify the objects in a faster way taking CNN as its foundation. In this paper, we introduce a model to identify and classify people wearing ID card.Our model uses an object detection system called YOLO (You Only Look Once) for detecting and classifying objects in real-time videos. In the YOLO algorithm, a single convolutional network predicts the bounding boxes and the class probabilities for these boxes. We aim to use our model for authentication, surveillance and security purposes at organizations, corporations and educational institutions to detect an unauthorized person at the premises or somebody without a valid identification document. Using the object detection and classification, we aim to build a model which would alert the respective authorities on the matter.
Keywords: Convolutional Neural Network, Object Detection and Classification, You Only Look Once(YOLO).
Scope of the Article: Classification