Implementation and Accuracy Evaluation of Fixed Camera-Based Object Positioning System Employing CNN-Detector

Today, different positioning applications such as location-based services and autonomous navigation are requiring more and more precision. Especially fully autonomous navigation requires accurate positioning solution, not only for the vehicle but also for the surrounding objects. Thus, many new positioning techniques, algorithms and fusion schemes have been developed. One essential technique is visual positioning. Thanks to intensive research in neural networks and deep learning, Convolutional Neural Network-based (CNN) object detectors have evolved greatly in recent years. This paper proposes a widely deployable scheme of fixed camera-based (e.g. surveillance camera) object positioning utilizing the CNN-detector. The accuracy of the implemented positioning solution is evaluated with precise Real-Time Kinematic (RTK) satellite positioning receiver. The implemented system can be used in indoors and outdoors, and it can estimate simultaneously positions from multiple camera views for multiple objects in real-time. When positioning a person, the measured mean positioning error was 10.7-15.6 cm with a simple bias correction and a standard deviation was 6.7-8.7 cm. Thus, the accuracy is excellent and would be sufficient to wide variety of applications.