IODTDLCNN: Implementation of Object Detection and Tracking by using Deep Learning based Convolutional Neural Network

Abstract

Video object detection plays the major role in variety applications including security, remote sensing and hyperspectral. Deep learning-based algorithms have made significant advances in video object recognition in recent years. The conventional machine learning applications are resulted in poor accuracy. In this article, a unified deep learning based convolutional neural network (DLCNN) is developed for composite multi object recognition in videos. To enhance composite object recognition, DLCNN analyses a composite item as a collection of background and adds part information into feature information. Correct component information may help forecast the shape and size of a feature data, which helps solve challenges caused by different forms and sizes of various objects. Finally, the DLCNN draws a bounding box to detected object by using the background features. Further, the simulation results shows that the performance of proposed method is improved as compared to the state of art approaches.