Centralized Multi-target Tracking based on Collaborative Sensorbox Perception using Camera and Radar in Infrastructure

In scalable traffic analysis for smart city applications and the increasing trend of autonomous driving in the foreseeable future, the implementation of multiple sensor systems for environmental perception will become essential. With the possibility of raw data transmission using 5G communication technology, a centralized approach using sensor data fusion of multiple sensors has been implemented to provide better collaborative environmental perception in infrastructure. Multi-target tracking plays an important role in environmental perception and has attracted enormous interest and effort in the research community resulting in approaches with different benefits and shortcomings. The thesis focuses on the implementation and evaluation of different types of model-based approaches for data association problems, namely Global Nearest Neighbour (GNN), Joint Probabilistic Data Association (JPDA) and Multiple Hypothesis Tracking (MHT) in the context of centralized multi-target tracking. The thesis presents methods for data processing and measurement mapping from sensor coordinates to world coordinates. The thesis also presents the use of homography to determine the position and orientation of the sensors. The implementation of the trackers is conducted in MATLAB and the evaluation of the trackers is performed using GOSPA metrics. The results indicate that GNN achieves optimal performance when targets are spatially distant and is computationally the most cost effective. JPDA demonstrates superior efficacy when targets are in close proximity. Overall, the performance of MHT is compromised by its computational expense.