  Edge computing efficiently extends the realm of information technology beyond
the boundary defined by cloud computing paradigm. Performing computation near
the source and destination, edge computing is promising to address the
challenges in many delay-sensitive applications, like real-time human
surveillance. Leveraging the ubiquitously connected cameras and smart mobile
devices, it enables video analytics at the edge. In recent years, many smart
video surveillance approaches are proposed for object detection and tracking by
using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This
work explores the feasibility of two popular human-objects detection schemes,
Harr-Cascade and HOG feature extraction and SVM classifier, at the edge and
introduces a lightweight Convolutional Neural Network (L-CNN) leveraging the
depthwise separable convolution for less computation, for human detection.
Single Board computers (SBC) are used as edge devices for tests and algorithms
are validated using real-world campus surveillance video streams and open data
sets. The experimental results are promising that the final algorithm is able
to track humans with a decent accuracy at a resource consumption affordable by
edge devices in real-time manner.
