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DATA SET GENERATION USING DEEP LEARNING ALGORITHMS AND VISUAL FEATURE TRACKING

By Kusuma Pallapotu

Abstract

Object detection and classification plays a major role in today\u27s modern technology. The implementations of these concepts range from consumer products to self driving cars. These concepts largely reply on the data sets used for training these models. There is a considerable amount of effort in generating these data sets for every specific application of these algorithms. In this report, a method for generating image data sets with the use of visual feature tracking and deep learning algorithms for application in autonomous vehicles has been proposed. The aim is to reduce the time and effort dedicated towards the generation of these application specific data sets. For this purpose, a software has been developed in Python for a Linux based system using Tensorflow, Keras, Pygames and OpenCV libraries which is capable of tracking an object of interest in a given media input specified by the user along with detecting various similar objects using a pre-trained Classification neural network. This software then compiles a file containing all the annotations for the above specified objects

Topics: Dataset generation, Visual feature tracking, object detection, Training samples, Artificial Intelligence and Robotics, Other Computer Sciences
Publisher: Digital Commons @ Michigan Tech
Year: 2019
OAI identifier: oai:digitalcommons.mtu.edu:etdr-1955

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