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.

本文提出了一种轻量级人群密度估计模型（LCDnet）和一个改进的训练方法（CL），并在无人机视频监控中实现了实时计数；实验结果表明，LCDnet 在一定范围内准确性较高，同时显著降低了推理时间和内存需求，可以在计算资源非常有限的边缘设备上部署。

LCDnet：用于实时视频监控的轻量级人群密度估计模型

LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance

Real-time video surveillance has become a crucial technology for smart
cities, made possible through the large-scale deployment of mobile and fixed
video cameras. In this paper, we propose situation-aware streaming, for
real-time identification of important events from live-feeds at the source
rather than a cloud based analysis. For this, we first identify the frames
containing a specific situation and assign them a high scale-of-importance
(SI). The identification is made at the source using a tiny neural network
(having a small number of hidden layers), which incurs a small computational
resource, albeit at the cost of accuracy. The frames with a high SI value are
then streamed with a certain required Signal-to-Noise-Ratio (SNR) to retain the
frame quality, while the remaining ones are transmitted with a small SNR. The
received frames are then analyzed using a deep neural network (with many hidden
layers) to extract the situation accurately. We show that the proposed scheme
is able to reduce the required power consumption of the transmitter by 38.5%
for 2160p (UHD) video, while achieving a classification accuracy of 97.5%, for
the given situation.

本研究提出一种基于情景感知的实时流式传输方案，以实时识别重要事件和降低发射功率。该方案主要利用少量隐藏层的小型神经网络在源端对特定情景下的帧进行高重要性分配，并将高重要性的帧与一定信噪比的低重要性帧同时流式传输，在接收端进行深度神经网络分析提取准确的情景并节约约 38.5% 的能源。