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Edge computing pushes the cloud computing boundaries beyond uncertain networkresource by leveraging computational processes close to the source and targetof data. Time-sensitive and data-intensive video surveillance applicationsbenefit from on-site or near-site data mining. In recent years, many smartvideo surveillance approaches are proposed for object detection and tracking byusing Artificial Intelligence (AI) and Machine Learning (ML) algorithms.However, it is still hard to migrate those computing and data-intensive tasksfrom Cloud to Edge due to the high computational requirement. In this paper, weenvision to achieve intelligent surveillance as an edge service by proposing ahybrid lightweight tracking algorithm named Kerman (Kernelized Kalman filter).Kerman is a decision tree based hybrid Kernelized Correlation Filter (KCF)algorithm proposed for human object tracking, which is coupled with alightweight Convolutional Neural Network (L-CNN) for high performance. Theproposed Kerman algorithm has been implemented on a couple of single boardcomputers (SBC) as edge devices and validated using real-world surveillancevideo streams. The experimental results are promising that the Kerman algorithmis able to track the object of interest with a decent accuracy at a resourceconsumption affordable by edge devices.

Kerman: A Hybrid Lightweight Tracking Algorithm to Enable Smart Surveillance as an Edge Service