  Edge computing allows more computing tasks to take place on the decentralized
nodes at the edge of networks. Today many delay sensitive, mission-critical
applications can leverage these edge devices to reduce the time delay or even
to enable real time, online decision making thanks to their onsite presence.
Human objects detection, behavior recognition and prediction in smart
surveillance fall into that category, where a transition of a huge volume of
video streaming data can take valuable time and place heavy pressure on
communication networks. It is widely recognized that video processing and
object detection are computing intensive and too expensive to be handled by
resource limited edge devices. Inspired by the depthwise separable convolution
and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural
Network (LCNN) is introduced in this paper. By narrowing down the classifier's
searching space to focus on human objects in surveillance video frames, the
proposed LCNN algorithm is able to detect pedestrians with an affordable
computation workload to an edge device. A prototype has been implemented on an
edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance
is achieved using real world surveillance video streams. The experimental study
has validated the design of LCNN and shown it is a promising approach to
computing intensive applications at the edge.
