An Intelligent Real-Time Occupancy Monitoring System Using Single Overhead Camera

Ahmad, Jawad; Larijani, Hadi; Emmanuel, Rohinton; Mannion, Mike; Javed, Abbas

Abstract

Real-time occupancy monitoring information is an important component in building energy management and security. Advances in technology enables us to develop vision-based systems. These systems have gained popularity among different scientific research communities due to their high accuracy. Based on real-time video from a single camera, people occupancy rates in buildings can be correctly estimated using neural network models. This paper proposes an intelligent real-time bidirectional system, using Random Neural Network (RNN) predictions. An overhead camera was used to capture RGB images and the number of people crossing a virtual line was counted using the proposed counting technique. The proposed algorithm extracts some important features such as occupant blob areas, major axis, minor axis, eccentricity, perimeters and area-perimeter ratio for total 1000 frames. Finally, a RNN model is trained with aforementioned features using a gradient decent algorithm. Real-time experimental results show the effectiveness of the proposed method, especially when occupants are in group and blob merge/split scenarios. Real-time testing revealed an accuracy between 100 and 93.38% for single and multiple occupants, respectively.

Citation

Ahmad, J., Larijani, H., Emmanuel, R., Mannion, M., & Javed, A. (2018, September). An Intelligent Real-Time Occupancy Monitoring System Using Single Overhead Camera. Presented at SAI Intelligent Systems Conference, London

• Presentation Conference Type: Conference Paper (published)
• Conference Name: SAI Intelligent Systems Conference
• Start Date: Sep 6, 2018
• End Date: Sep 7, 2018
• Online Publication Date: Nov 8, 2018
• Publication Date: 2019
• Deposit Date: Sep 13, 2019
• Publisher: Springer
• Pages: 957-969
• Series Title: Advances in Intelligent Systems and Computing
• Series Number: 869
• Book Title: Proceedings of SAI Intelligent Systems Conference
• ISBN: 9783030010560; 9783030010577
• DOI: https://doi.org/10.1007/978-3-030-01057-7_71
• Public URL: http://researchrepository.napier.ac.uk/Output/2133598