Bayesian data fusion approaches for vehicle video analytics
- Publication Type:
- Thesis
- Issue Date:
- 2007
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
01Front.pdf | contents and abstract | 788.63 kB | |||
02Whole.pdf | thesis | 61.57 MB |
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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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