Toward Intelligent Surveillance as an Edge Network Service (iSENSE) Using Lightweight Detection and Tracking Algorithms

Edge computing extends the realm of information technology beyond the boundaries defined by cloud computing. Performing computation near the sensors, edge computing is promising to address the challenges in many bandwidth-and delay-sensitive applications. Although recently many smart video surveillance approaches based on Machine Learning (ML) algorithms become available, it is still challenging to efficiently migrate those smart algorithms to edge. In this paper, we propose an intelligent Surveillance as an Edge Network Service (iSENSE), which explores the feasibility of moving ML to the edge by testing two popular human-object detection schemes. Besides, a lightweight Convolutional Neural Network (L-CNN) is introduced to improve computational execution by leveraging the depth-wise separable convolution. To enhance performance on edge, we propose a hybrid lightweight tracking algorithm, Kerman (Kernelized Kalman filter), which is a decision tree based hybrid Kernelized Correlation Filter algorithm designed for human-object tracking. We have implemented both Kerman and L-CNN algorithms on edge by using different types of single board computers. The proposed iSENSE system was validated using both real-world campus surveillance video and open image sets. The experimental results present that the proposed algorithms can track the human objects in real-time with a good accuracy with limited resource in edge devices.