LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance
This addresses the need for efficient crowd counting in video surveillance using drones with limited computing resources, though it is incremental as it builds on existing CNN-based methods.
The paper tackles the problem of high inference delay in crowd density estimation models for real-time video surveillance by proposing LCDnet, a lightweight model that achieves reasonably good accuracy while significantly reducing inference time and memory requirements, as evaluated on DroneRGBT and CARPK datasets.
Automatic crowd counting using density estimation has gained significant attention in computer vision research. As a result, a large number of crowd counting and density estimation models using convolution neural networks (CNN) have been published in the last few years. These models have achieved good accuracy over benchmark datasets. However, attempts to improve the accuracy often lead to higher complexity in these models. In real-time video surveillance applications using drones with limited computing resources, deep models incur intolerable higher inference delay. In this paper, we propose (i) a Lightweight Crowd Density estimation model (LCDnet) for real-time video surveillance, and (ii) an improved training method using curriculum learning (CL). LCDnet is trained using CL and evaluated over two benchmark datasets i.e., DroneRGBT and CARPK. Results are compared with existing crowd models. Our evaluation shows that the LCDnet achieves a reasonably good accuracy while significantly reducing the inference time and memory requirement and thus can be deployed over edge devices with very limited computing resources.