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

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