A Lightweight and Efficient Convolutional Neural Network for Crowd Counting

The crowd counting task plays a key role in ensuring public safety during large gatherings events. Most prominent works in this area, use large and computationally demanding deep learning model architectures, which require substantial computational power, limiting their usage in a real-world scenario under resource constraints. In this work we consider the trade-off between the model’s predicted accuracy and computational speed. We propose an improved version of HR-Net, which is substantially smaller and faster than the original, but preserves its localization and counting performance. Through targeted removal of unnecessary modules and branches, we demonstrate an increase in frames-per-second by 37.71% on an Nvidia Jetson Orin, and a reduction of GMACs and parameters by 77.41% and 73.07% respectively, while retaining competitive localization and counting performance, specifically for aerial imagery scenarios. Our modifications enable the algorithm to process in real-time higher resolution images, which is crucial when dealing with small objects. Furthermore, because most crowd counting datasets contain random images gathered from the web, and limited aerial images of crowds, we introduce a specialized dataset of high-resolution aerial imagery for sparse and dense crowds in various environments, that contains new drone-captured annotated image data.