Lightweight real-time Object Tracking based on Pixel and GPS for Mobile Surveillance Platform

Abstract

This study proposed a method for detecting moving objects (e.g., people and vehicles) in the images of a lightweight mobile platform, and for the real-time visualization and tracking of the location information on the screen. First, a lightweight cable-driven platform and remote access environment were configured for the experiment. Next, a TensorFlow Lite environment was configured in the control computer, and an object detection algorithm was applied to the images obtained from the camera on the cable-driven platform. Subsequently, the bounding box coordinates of the object and class information were used as the inputs, as well as the camera specifications, GPS information, and the actual size information of the object to estimate the coordinates of the object. However, the significant limitations in the performance of CPU, GPU, and memory restrict the applications of high-performance deep learning models in a mobile platform environment. Therefore, in this study, a lightweight model, such as MobileNet SSD v2 was applied, and Google Edge TPU, which can use TensorFlow Lite environment, was used to facilitate computations, such as real-time object detection and coordinate estimation. In addition, this study presented a method for assigning a unique ID to a detected object and tracking it using the Euclidean distance, and the advantages of a simple algorithm using GPS data and a single moving camera in the surveillance field were presented.