Using passive infrared sensors is a well-established technique of presence monitoring. While it can significantly reduce energy consumption, more savings can be made when utilising more modern sensor solutions coupled with machine learning algorithms. This paper proposes an improved method of presence monitoring, which can accurately derive the number of people in the area supervised with a low-cost and low-energy thermal imaging sensor. The method utilises U-Net-like convolutional neural network architecture and has a low parameter count, and therefore can be used in embedded scenarios. Instead of providing simple, binary information, it learns to estimate the occupancy density function with the person count and approximate location, allowing the system to become considerably more flexible. The tests show that the method compares favourably to the state of the art solutions, achieving significantly better results.
Recent advances in deep learning-based image processing have enabled significant improvements in multiple computer vision fields, with crowd counting being no exception. Crowd counting is still attracting research interest due to its potential usefulness for traffic and pedestrian stream monitoring and analysis. This study considered a specific case of crowd counting, namely, counting based on low-altitude aerial images collected by an unmanned aerial vehicle. We evaluated a range of neural network architectures to find ones appropriate for on-board image processing using edge computing devices while minimising the loss in performance. Through experiments on a range of neural network architectures, we also showed that the input image resolution significantly impacts the prediction quality and should be considered an important factor before going for a more complex neural network model to improve accuracy. Moreover, by extending a state-of-the-art benchmark with more in-depth testing, we showed that larger models might be prone to overfitting because of the relative scarcity of training data.
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