ARCN: A Real-time Attention-based Network for Crowd Counting from Drone Images

Abstract

Crowd counting is the process of counting or estimating the number of individuals in a crowd. There has been a rapid surge in the amount of Unmanned Aerial Vehicles (UAV) images over the last few years. However, efficient crowd counting techniques from UAV images have hardly come into the focus of the research community. Crowd counting from UAV images has its unique challenges compared to crowd counting from images in natural scenes. Moreover, solving the problem in real-time makes the task even harder.In this paper, we introduce an attention-based encoder-decoder model called Attention-based Real-time CrowdNet (ARCN). ARCN is a computationally efficient density estimation-based crowd counting model. It can perform crowd-counting from UAV images in real-time with high accuracy. Ours is the first work that proposes a real-time density map estimation and crowd counting model from drone-based images. The key idea of our work is to add “Convolution Block Attention Module” (CBAM) blocks in-between the bottleneck layers of the MobileCount architecture. The proposed ARCN model achieves an MAE of 19.9 and MSE of 27.7 on the DroneCrowd dataset. Also, on NVIDIA GTX 2080 Ti GPU, ARCN has a processing speed of 48 FPS, making it a real-time technique. The pre-trained model is available at https://bit.ly/3na7LUy