Qualitative and structural analysis of video sequences.
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This thesis analyses videos in two distinct ways so as to improve both human understanding and the computer description of events that unfold in video sequences. Qualitative analysis can be used to understand a scene in which many details are not needed. However, for there to be an accurate interpretation of a scene, a computer system has to first evaluate discretely the events in a scene. Such a method must involve structural features and the shapes of the objects in the scene. In this thesis we perform qualitative analysis on a road scene and generate terms that can be understood by humans and that describe the status of the traffic and its congestion. Areas in the video that contain vehicles are identified regardless of scale. The movement of the vehicles is further identified and a rule-based technique is used to accurately determine the status of the traffic and its congestion. Occlusion is a common problem in scene analysis tracking. A novel technique is developed to vertically separate groups of people in video sequences. A histogram is generated based on the shape of a group of people and its valleys are identified. A vertical seam for each valley is then detected using the intensity of the edges. This is then used as the separation boundary between the different individuals. This could definitely improve the tracking of people in a crowd. Both techniques achieve good results, with the qualitative analysis accurately describing the status and congestion of a traffic scene, while the structural analysis can separate a group of people into distinctly separate persons.