Nitin Budania,
Mann Sethi,
Mohit Jeswani,
Khushi Agarwal,
Neetu Joshi,
- Student Department of Computer Engineering, Poornima College of Engineering, Jaipur Rajasthan India
- Student Department of Computer Engineering, Poornima College of Engineering, Jaipur Rajasthan India
- Student Department of Computer Engineering, Poornima College of Engineering, Jaipur Rajasthan India
- Student Department of Computer Engineering, Poornima College of Engineering, Jaipur Rajasthan India
- Assistant Professor Department of Computer Engineering, Poornima College of Engineering, Jaipur Rajasthan India
Abstract
Real-time image processing applications using OpenCV encompass a diverse and crucial array of tasks in modern technology. OpenCV’s robust capabilities enable the implementation of object detection and tracking, which are vital for surveillance systems to monitor and analyze activities in real time. In the realm of security, face recognition technology, powered by OpenCV, provides accurate and efficient identification and authentication, enhancing safety measures. Gesture recognition, another significant application, facilitates intuitive human-computer interaction, allowing users to control devices through natural movements. Moreover, OpenCV’s support for document scanning applications plays a pivotal role in the digitization of physical documents. Through advanced text extraction and optical character recognition techniques, OpenCV converts paper documents into editable and searchable digital formats, streamlining workflows and improving accessibility. These applications not only demonstrate OpenCV’s versatility but also its practicality in addressing real-world challenges. OpenCV’s contributions extend across numerous domains, from security and surveillance to digital documentation and interactive technologies. By providing a comprehensive platform for these diverse applications, OpenCV drives significant advancements in computer vision technology, paving the way for future innovations and practical solutions in an increasingly digital world.
Keywords: Image processing, OpenCV, face detection, Haar cascade files, artificial intelligence (AI) algorithms
[This article belongs to Journal of Open Source Developments(joosd)]
References
- Tejaswini PD, Abhiram D, Yasaswini C, Babu GS, Hithish D, Rao S. Music recommendation based on real-time emotion detection. Int J Gender Sci Technol. 2024; 13 (1): 1–11.
- Emami S, Suciu VP. Facial recognition using OpenCV. J Mobile Embed Distrib Syst. 2012; 4 (1): 38–43.
- Phansalkar S, Sonkamble RG. ChatGenie – an autonomous chatbot using AI with AI detection and image generation. Int J Novel Res Dev. 2023; 8 (5): d610–d621.
- Mishra R, Dubey V, Rajbhar S. WhatsApp chat analysis for government organization using machine learning. Int J Adv Res Sci Commun Technol. 2024; 4 (6): 241–245.
- Parveen S, Shah J. A motion detection system in Python and OpenCV. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, February 4–6, 2021. pp. 1378–1382.
- Mishra S, Thanh LT. SATMeas – object detection and measurement: canny edge detection algorithm. In: Pan X, Jin T, Zhang L-J, editors. Proceedings of the International Conference on AI and Mobile Services 2022. Cham, Switzerland: Springer International Publishing; 2022. pp. 91–101.
- Misnevs B, Kabashkin I, Uzule K. Education mobility as a service: model of the ecosystem. In: Kabashkin I, Yatskiv I, Prentkovskis O, editors. Proceedings of the International Conference on Reliability and Statistics in Transportation and Communication 2021. Cham, Switzerland: Springer International Publishing; 2021. pp. 598–607.
- Ismail A, Awad M. Towards cross-disaster building damage detection with graph convolutional networks. In: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, July 17–22, 2022. pp. 223–226.
- Pawar P, Lad A, Thailiani M, Bhagwani L, Pachpande P, Pednekar P, Wankhade S. AI influencer with assistant. Int J Res Publ Rev. 2024; 5 (3): 3496–3500.
- Pietruch RW, Grzanka AD. Vowel recognition of patients after total laryngectomy using mel frequency cepstral coefficients and mouth contour. J Automat Control. 2010; 20 (1): 33–38.
Journal of Open Source Developments
Volume | 11 |
Issue | 02 |
Received | May 6, 2024 |
Accepted | July 31, 2024 |
Published | August 7, 2024 |