Edge computing-based person detection system for top view surveillance: Using CenterNet with transfer learning

Edge computing significantly expands the range of information technology in smart video surveillance applications in the era of intelligent and connected cities. Edge devices, including Internet of Things-based cameras and sensors, produce a large amount of data and have frequently become prominent components for various public surveillance and monitoring applications. The data generated by these smart devices are in the form of videos and images that need to be processed and analyzed in real-time with substantial computation resources. These developed techniques still require large computation resources for real-time surveillance applications. In this regard, Edge computing plays a promising role in order to provide high computation and low-latency requirements. With these motivations, in this work, a real-time top view-based person detection system is presented. We utilize a one-stage deep learning-based object detection algorithm, i.e., CenterNet, for person detection. The model detects the human as a single point, also referred to as its bounding box's center point. The model does a key-point calculation to obtain the center point and regresses all other information regarding the target object's features, size, location, and orientation. Training and testing of the model are performed on a top view data set. The detection results are also compared with conventional detection methods using the same data set. The overall detection accuracy of the model is 95%.