Recognition and Detection of Content in Video Using OpenCV

Open Access

Year : 2021 | Volume : | Issue : 2 | Page : 42-47
By

    Akshit Rawat

  1. Student, Department of Computer Science & Engineering, Dr. M.C. Saxena College of Engineering & Technology, Uttar Pradesh, India

Abstract

The emergence and continued reliance on the Internet and related technologies has resulted in massive amounts of data that can be analysed. Humans, on the other hand, do not have the cognitive abilities to comprehend such vast amounts of data. Machine learning (ML) is a mechanism that enables humans to process large amounts of data, gain insights into the data’s behaviour, and make more informed decisions based on the analysis’s results. ML has a wide range of applications, such as efficient and accurate object detection, and has been a hot topic in the advancement of computer vision systems. Since the introduction of deep learning techniques, the accuracy of object detection has increased dramatically. The project intends to incorporate cutting-edge object detection techniques with the goal of achieving high accuracy with real-time performance. The reliance on other computer vision techniques to assist the deep learning-based approach is a major challenge in many object detection systems, resulting in slow and suboptimal performance. The resulting system is fast and accurate, making it useful for applications that require object detection.

Keywords: Recognition, Detection, OpenCV, E-R, DFD

[This article belongs to International Journal of Image Processing and Pattern Recognition(ijippr)]

How to cite this article: Akshit Rawat Recognition and Detection of Content in Video Using OpenCV ijippr 2021; 7:42-47
How to cite this URL: Akshit Rawat Recognition and Detection of Content in Video Using OpenCV ijippr 2021 {cited 2021 Dec 31};7:42-47. Available from: https://journals.stmjournals.com/ijippr/article=2021/view=90342

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Regular Issue Open Access Article
Volume 7
Issue 2
Received December 22, 2021
Accepted December 28, 2021
Published December 31, 2021