Real-Time WebRTC based Mobile Surveillance System

Authors

  • Alistair Baretto Student, Department of Computer Engineering, Fr. C. Rodrigues Institute of Technology, Navi Mumbai, INDIA
  • Noel Pudussery Student, Department of Computer Engineering, Fr. C. Rodrigues Institute of Technology, Navi Mumbai, INDIA
  • Veerasai Subramaniam Student, Department of Computer Engineering, Fr. C. Rodrigues Institute of Technology, Navi Mumbai, INDIA
  • Amroz Siddiqui Assistant Professor, Department of Computer Engineering, Fr. C. Rodrigues Institute of Technology, Navi Mumbai, INDIA

DOI:

https://doi.org/10.31033/ijemr.11.3.4

Keywords:

Computer Vision, Deep learning, WebRTC, YOLO, Android Development, REST API

Abstract

The rapid growth that has taken place in Computer Vision has been instrumental in driving the advancement of Image processing techniques and drawing inferences from them. Combined with the enormous capabilities that Deep Neural networks bring to the table, computers can be efficiently trained to automate the tasks and yield accurate and robust results quickly thus optimizing the process. Technological growth has enabled us to bring such computationally intensive tasks to lighter and lower-end mobile devices thus opening up a wide range of possibilities. WebRTC-the open-source web standard enables us to send multimedia-based data from peer to peer paving the way for Real-time Communication over the Web. With this project, we aim to build on one such opportunity that can enable us to perform custom object detection through an android based application installed on our mobile phones. Therefore, our problem statement is to be able to capture real-time feeds, perform custom object detection, generate inference results, and appropriately send intruder alerts when needed. To implement this, we propose a mobile-based over-the-cloud solution that can capitalize on the enormous and encouraging features of the YOLO algorithm and incorporate the functionalities of OpenCV’s DNN module for providing us with fast and correct inferences.  Coupled with a good and intuitive UI, we can ensure ease of use of our application.

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Published

2021-06-30

How to Cite

Alistair Baretto, Noel Pudussery, Veerasai Subramaniam, & Amroz Siddiqui. (2021). Real-Time WebRTC based Mobile Surveillance System. International Journal of Engineering and Management Research, 11(3), 30–35. https://doi.org/10.31033/ijemr.11.3.4

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Section

Articles