Bayesian data fusion approaches for vehicle video analytics

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NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Vehicle Video Analytics (WA) deals with the analysis of the data from a video associated with car images and automatically picking out the information that identifies the vehicle. For example, Licence Plate Recognition (LPR) is a one of the WA applications dealing with the identification of the plates automatically, however, it may not be enough to completely characterise the vehicle; vehicles' features include colour, model, size, speed, shape and number of axles. Combining multiple vehicles' features in a framework will be useful in many applications including stolen car retrieval, petrol theft, car park management and road asset protection. Inspired by these applications, the thesis introduces a comprehensive framework for a vehicle video analytics to identify the many features of vehicles. Several new and improved algorithms are proposed to extract and recognise vehicles features along with image enhancement methods to boost the over all performance of the WA systems. Most LPR systems perform well in a controlled environment or when the effect of noise on the image processing algorithms is small. However, in realistic situations, random noise occurs for many reasons; such as variations in lighting conditions, image quality and additional noise from image sensors. The impact of the random noise can be reduced using information from multiple frames/thresholds. This thesis proposes novel multi frame/threshold data fusion approaches to reduce the effect of the external and internal random noise. Licence plate localisation algorithms are exploited from thresholding techniques (Otsu, Hysteresis, etc.) in order to extract the plate from the image; however, the thresholding does not segment the plate under various illumination conditions. We propose an approach of threshold variation which ensures the object is to be segmented at least once, the dusters are modelled as a joint distribution of their features, and all data from different thresholds update the posterior using Bayesian inference. In the recognition part, we propose a novel approach for increasing the accuracy for any particular character recognition technique by taking advantage of the available frames. Each frame result acts as an individual sensor which informs its recognition results. These results from each individual frame (sensor) are fused to obtain the final recognition result. Simple approaches for the speed, model and colour of the vehicle based on the plate location are presented. The speed is estimated through out tacking the plate across frames. The approaches of plate extraction and recognition are exploited for emblem recognition as well. Since colours can be seen differently under different illuminations, we use illumination estimation and correction for the image before fusing the colour histograms of three areas around the plate which represent vehicle's colour. Extensive experimental test were carried using real data collected on high ways and car parks. Through out the thesis, the fusion problem formulation in the context of WA is illustrated and performance improvements are demonstrated.
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