This is a repository copy of Spatio-temporal Gaussian process models for extended and group object tracking with irregular shapes.

Model-based approaches for target tracking and smoothing estimate the infinite number of possible target trajectories using a finite set of models. This paper proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions. Recursive Gaussian process and derivative based Gaussian process approaches for target tracking and smoothing are developed, with online training and parameter learning. The performance evaluation over two highly maneuvering scenarios, shows that the proposed approach provides 80% and 62% performance improvement in the position and 49% and 22% in the velocity estimation, respectively, as compared to the best model-based filter.

Abstract-Tracking of arbitrarily shaped extended objects is a complex task due to the intractable analytical expression of measurement to object associations. The presence of sensor noise and clutter worsens the situation. Although a significant work has been done on the extended object tracking (EOT) problems, most of the developed methods are restricted by assumptions on the shape of the object such as stick, circle, or other axis-symmetric properties etc. This paper proposes a novel Gaussian process approach for tracking an extended object using a convolution particle filter (CPF). The new approach is shown to track irregularly shaped objects efficiently in presence of measurement noise and clutter. The mean recall and precision values for the shape, calculated by the proposed method on simulated data are around 0.9, respectively, by using 1000 particles.

Target tracking performance relies on the match between the tracker motion model and the unknown target dynamics. The performance of these model-based trackers degrades when there is a mismatch between the model and the target motion. In this paper, a Gaussian process based approach, namely, Gaussian process motion tracker (GPMT) is proposed. The Gaussian process framework is flexible and can represent an infinite number of motion modes. The evaluation of the proposed approach is performed on challenging scenarios and is compared with popular single and multiple-model based approaches. The results show high accuracy of the predicted and estimated target position and velocity over challenging maneuver scenarios.

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.