Object Tracking over Sensor Networks

Object tracking is an important problem that has been of interest to researchers from different fields. Tracking algorithms are used in a wide variety of domains, such as robotics, vehicular traffic, navigation and communication systems. The main goal is to obtain a record of the trajectory of the moving object(s) over space and time by processing the sensor data. Reliable tracking methods are of crucial importance in many surveillance systems in order to enable human operators to remotely monitor activity across large environments such as: a) transport systems (e.g., railway transportation, airports, urban and motorway road networks, and maritime transportation), b) banks, shopping malls, car parks, and public buildings, c) industrial environments, and d) government establishments (military bases, prisons, strategic infrastructures, radar centres, and hospitals). The problem has different particularities depending on whether data from one or multiple sensors are used. Sensor networks offer many advantages due to the fact that the coming data provide a global picture from different sides. Multiple-sensor systems can provide surveillance coverage across a wide area, ensuring constant object visibility. However, the presence of data from multiple sensors poses many new challenges from theoretical and practical point of view that will be addressed in this project. How to efficiently process the data from many sensors is a significant problem. The data rates associated with collection assets can vary greatly, for instance between a measurement once from each day from a satellite to a measurement every twenty-fifth of a second from a camera. It is impossible to define a single tracking methodology and technique that meet the requirements of all these domains. Advanced tracking algorithms can combine the functionality of existing identification and tracking processes while accounting for any uncertainty if present.Different techniques will be developed in this project outperforming the previously existing techniques in the literature, which will be suitable for on-line implementations. The main interest will be focussed on innovative Bayesian techniques, such as sequential Monte Carlo methods (also called particle filters), Monte Carlo Markov chains and Unscented Kalman filtering, providing efficient approximations to the optimal Bayesian solutions. The Monte Carlo approach is generic, scalable, flexible and has opportunities for parallelisation and distributed implementation. Monte Carlo methods afford natural incorporation of constraints which is difficult or impossible with standard filtering techniques. The algorithms will be implemented in a centralised and distributed way, which is a novel and significant achievement. It can save a lot of energy and reduce the communication load of the supporting sensor networking systems, it will increase its robustness to failure and respectively the reliability of the tracking module. The innovative elements of this proposal rely on the powerful methodology and the focus on very important problems that have been in the scope of interest of scientists and engineers. Problems such as group object tracking and distributed particle filtering represent substantial research challenges which makes this research unique.