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.
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Ahmad, J., Larijani, H., Emmanuel, R., Mannion, M., Javed, A. (2019). An Intelligent Real-Time Occupancy Monitoring System Using Single Overhead Camera. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_71
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