Multi-object tracking in video using labeled random finite sets

Abstract

The safety of industrial mobile platforms (such as fork lifts and boom lifts) is of major concern in the world today as industry embraces the concepts of Industry 4.0. The existing safety methods are predominantly based on Radio Frequency Identification (RFID) technology and therefore can only determine the distance at which a pedestrian who is wearing an RFID tag is standing. Other methods use expensive laser scanners to map the surrounding and warn the driver accordingly. The aim of this research project is to improve the safety of industrial mobile platforms, by detecting and tracking pedestrians in the path of the mobile platform, using readily available cheap camera modules. In order to achieve this aim, this research focuses on multi-object tracking which is one of the most ubiquitously addressed problems in the field of \textit{Computer Vision}. Algorithms that can track targets under severe conditions, such as varying number of objects, occlusion, illumination changes and abrupt movements of the objects are investigated in this research project. Furthermore, a substantial focus is given to improving the accuracy and, performance and to handling misdetections and false alarms. In order to formulate these algorithms, the recently introduced concept of Random Finite Sets (RFS) is used as the underlying mathematical framework. The algorithms formulated to meet the above criteria were tested on standard visual tracking datasets as well as on a dataset which was created by our research group, for performance and accuracy using standard performance and accuracy metrics that are widely used in the computer vision literature. These results were compared with numerous state-of-the-art methods and are shown to outperform or perform favourably in terms of the metrics mentioned above