Paper Title :Realtime Object Detection Using Deep Learning

Author :Kavita Sahu, Monika Vishwakarma, Parineeta Jha

Article Citation :Kavita Sahu ,Monika Vishwakarma ,Parineeta Jha , (2023 ) " Realtime Object Detection Using Deep Learning " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 80-83, Volume-11,Issue-5

Abstract : As object recognition technology has developed recently, various technologies have object detection technique for blind people in real time to detect objects on any device running this model. We use Convolutional neural network along with single shot multi-box detector algorithm to develop the proposed model. This model is composed of multiple layers to classify the given objects into any of the defined classes. Video tracking is one of the fields of recent development in the field of computer vision. A lot of research has been going on in this area and new algorithms are being proposed to detect and track objects in video. This field of study has experienced a sudden growth especially after robotics has started gaining importance. The objective of this paper is to present the different steps involved in tracking objects in a video sequence, namely object detection, object classification and object tracking. We survey the different methods available for detecting, classifying, and tracking objects in a detailed manner. The pros and cons of each of the methods are discussed. According to the object detection definition object detection can be defined by identifying different objects automatically from image files. Implementing by multiple deep learning technique, many problems which occur frequently and disturb the accuracy can be improved. Convolutional neural network is currently the state-of-the-art solution for object detection. To improve and test object detection system is the main task of this project. This system is applied for images based on convocational neural network. Keywords - Object detection, Classifying, Object Tracking, CNN

Type : Research paper

Published : Volume-11,Issue-5

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