LiDAR-based Outdoor Crowd Management for Smart Campus on the Edge
Yitao Chen Krishna Gundu Zohair Zaidi Ming Zhao
Crowd management is crucial for a smart campus. Popular methods are
camera-based. However, conventional camera-based approaches may leak users'
personally identifiable features, jeopardizing user's privacy, which limits its
application. In this work, we investigate using affordable light detection and
ranging (LiDAR) technology to perform outdoor crowd management leveraging edge
computing. Specifically, we aim to count the number of people on a walkway of a
university campus. Besides privacy protection, LiDAR sensors are superior to
cameras since their performance will not be compromised when the campus is not
well-illuminated. We deploy LiDAR sensors on light poles to collect data from
the crowd on the campus and leverage edge accelerators to process data locally.
We proposed two different methodologies in this work: 1) a non-convolutional
neural network (CNN)-based approach, using clustering and autoencoder, and 2) a
CNN-based approach that first projects point clouds to 2D planes and then
processes the projection with conventional CNNs. Our first approach relies on
careful feature engineering, whereas our second approach does not require such
effort. However, the CNN-based approach requires more computational power than
our non-CNN-based approach. We evaluate both approaches comprehensively with
our hand-labeled real-life data collected from campus. Our evaluation results
show that the first method achieves an accuracy of 85.4%, whereas the second
method achieves 95.8%. Our CNN-based method outperforms existing solutions
significantly. We also deploy our two models on an edge accelerator, TPU, to
measure the speedup, leveraging this specialized accelerator.