Thida, Myo (2013) Contextual analysis of videos capturing multiple moving targets. (PhD thesis), Kingston University, .
Abstract
Over the last two decades, computer vision researchers have been working to improve the accuracy and robustness of algorithms for the context analysis of videos capturing single or multiple moving targets. However, devising algorithms that can work in uncontrolled environments with variable and unfavourable lighting conditions is still a major challenge. This thesis aims to develop robust methodologies to analyse scenes with multiple moving targets captured by a stationary camera. First, a new particle swarm optimisation algorithm is proposed to in-corporate social interaction among targets. A set of interactive swarms is employed to track multiple pedestrians in a crowd. The proposed method improves the standard particle swarm optimisation algorithm with a dynamic social model that enhances the interaction among swarms. In addition, constraints provided by temporal continuity and strength of person detections are incorporated in the tracking process. This allows the particle swarm optimisation algorithm to track multiple moving targets in a complex scene. Second, a novel method is proposed to detect global unusual events and accurately localise abnormal regions in the monitored scene. The idea is to exploit temporal coherence between video frames and use the manifold learning algorithm, in particular Laplacian Eigenmaps, to discover different crowd activities from a video. The proposed method provides an advantage of visualising and identifying different crowd events in a low dimensional space and detect abnormality. Then, this method is further extended to detect localised abnormality where the behaviour of an individual deviates from the rest of the crowd. In this approach. the visual contexts of multiple local patches are studied to model the regular behaviour of a crowded scene. This local probabilistic model allows to detect abnormal behaviour in both local and global context and localise the regions where abnormal behaviour occurs. The performance of the proposed algorithms is validated using standard data-sets and surveillance videos captured in uncontrolled environments.
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