  Edge computing pushes the cloud computing boundaries beyond uncertain network
resource by leveraging computational processes close to the source and target
of data. Time-sensitive and data-intensive video surveillance applications
benefit from on-site or near-site data mining. 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.
However, it is still hard to migrate those computing and data-intensive tasks
from Cloud to Edge due to the high computational requirement. In this paper, we
envision to achieve intelligent surveillance as an edge service by proposing a
hybrid 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 a
lightweight Convolutional Neural Network (L-CNN) for high performance. The
proposed Kerman algorithm has been implemented on a couple of single board
computers (SBC) as edge devices and validated using real-world surveillance
video streams. The experimental results are promising that the Kerman algorithm
is able to track the object of interest with a decent accuracy at a resource
consumption affordable by edge devices.
