Nicholas J. Redding

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Previously a particle filter has been proposed to detect colour objects in video [1]. In this work, the particle filter is adapted to track people in surveillance video. Detection is based on automated background modelling rather than a manually-generated object colour model. A labelling method is proposed that tracks objects through the scene rather than(More)
Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains(More)
Many computer vision methods rely on frame registration information obtained with algorithms such as the Kanade-Lucas-Tomasi (KLT) feature tracker, which is known for its excellent performance in that area. Various research groups proposed methods to extend its performance, both in terms of execution time and stability. Recent research has shown that(More)
This paper describes a moving target indication and tracking system developed for acquiring and tracking targets in video from moving sensors, in particular airborne urban surveillance video. The paradigm of the moving sensor, which is a typical scenario in defence applications (e.g. UAV surveillance video), poses some unique problems as compared to the(More)
This report presents a review of a video moving target indication (VMTI) capability implemented in the Analysts’ Detection Support System (ADSS). The VMTI subsystem has been devised for video from moving sensors, in particular, but not exclusively, airborne urban surveillance video. The paradigm of the moving sensor, which is a typical scenario in defence(More)
This report presents a review and classification of image registration methods that are either currently available in the Analyst’s Detection Support System (ADSS) or scheduled for implementation in ADSS in the near future. The aim of this report is to gain an overall understanding of our capabilities in the field of image registration, by identifying key(More)