On-Board Crowd Counting and Density Estimation Using Low Altitude Unmanned Aerial Vehicles-Looking beyond Beating the Benchmark

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.

Automated summary

This study investigates crowd counting using low-altitude aerial images and evaluates neural network architectures to enhance image processing performance. Experiments show that input image resolution significantly impacts prediction quality, and careful consideration should be given before opting for a more complex neural network model.