Pixel behaviour metrics for dynamic background modelling with the projected difference pattern method

US

Novel algorithms for tracking small and fast objects in low quality images.

In conventional computer vision systems, high image quality and long target exposure requirements are required. In this thesis, two algorithms to overcome such limitations of current computer vision systems have been proposed. The Pixel Exclusion Double Difference Algorithm (PEDDA) algorithm is a novel object detection algorithm that is able to detect fast moving objects in noisy images and suppress interference from large, low speed moving objects. The State-based “Observation, Analysis and Prediction” Target Election and Tracking Algorithm (SOAPtet) algorithm uses a deterministic state machine to guide the SOAPtet algorithm predictions. A novel stochastic based approach is also implemented in this algorithm to elect the target of interest from its candidates that are usually triggered by noise. A real time experimental system is developed based on the two algorithms. The experiment results show that this system detects up to 92.3% of moving objects in noisy environment and the tracking accuracy is up to 97.42%

Novelty detection in image sequences with dynamic background

We propose a new scheme for novelty detection in image sequences capable of handling non-stationary background scenarious, such as waving trees, rain and snow. Novelty detection is the problem of classifying new observations from previous samples, as either novel or belonging to the background class. An adaptive background model, based on a linear PCA model in combination with local, spatial transformations, allows us to robustly model a variety of appearences. An incremental PCA algorithm is used, resulting in a fast and efficient detection algorithm. The system has been successfully applied to a number of different (outdoor) scenarious and compared to other approaches