Crowd Counting Using Deep Learning in Edge Devices

Abstract

Crowd counting is required for many situations and has historically been undertaken using approximate (manual) estimations and measures. Deep learning allows to improve this situation. Modern crowd counting models are commonly based on pixel-wise density maps using deep convolutional neural networks (CNNs) comprising tens of millions of parameters. These models require high-performance GPUs for training and subsequent usage and inference. As such, these models are difficult to deliver to edge devices that have limited computing resources such as surveillance cameras, mobile phones and Internet of Things (IoT)-Type devices. This paper proposes a new method to tackle this issue based on three key components: feature fusion, Bayesian Loss [19] and datasets utilising bounding-box annotations to increase the efficiency of the crowd counting task. Experiments show that the proposed method can not only provide accuracy close to the latest state-of-The-Art deep learning models, but support real-Time inference in edge devices offering limited computational capacity.