Intelligent Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN

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 surveillance. Leveraging the ubiquitously connected cameras and smart mobile devices, it enables video analytics at the edge. However, traditional human-objects detection and tracking approaches are still computationally too expensive to edge devices. Aiming at intelligent surveillance as an edge network service, this work explored the feasibility of two popular humanobjects detection schemes, Harr-Cascade and SVM, at the edge. Understanding the existing constraints of the algorithms, a lightweight Convolutional Neural Network (L-CNN) is proposed using the depthwise separable convolution. The proposed L-CNN considerably reduces the number of parameters without affecting the quality of the output, thus it is ideal for an edge device usage. Being trained with Single Shot Multi-box Detector (SSD) to pinpoint each human-object location, it gives coordination of bounding box around the object. We implemented and tested L-CNN on an edge device using Raspberry PI 3. The intensive experimental comparison study has validated that the proposed L-CNN is a feasible design for real-time human-object detection as an edge service. Keywords—Edge Computing, Smart Surveillance, Lightweight Convolutional Neural Network (L-CNN), Human Detection.