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Video object detection plays a significant role in various applications, including security, remote sensing and hyperspectral. In recent years, deep learning-based algorithms have made significant advances in video object recognition. The conventional machine learning applications have 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. DLCNN analyses a composite item like a collection of background and adds part information into feature information to enhance hybrid object recognition. 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 detect objects using background features. Further, the simulation results show that the proposed method's performance is improved compared to the state of art approaches.


Object detection Deep learning-based convolutional neural network

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How to Cite
A., A. M. . (2022). Implementation of Object Detection and Tracking by Using Deep Learning Based Convolutional Neural Networks. Convergence Chronicles, 3(1), 503–510.