| dc.description.abstract |
Typical Automatic Number Plate Recognition (ANPR) system uses high resolution cameras to acquire good quality images of the vehicles passing through. In these images, license plates are localized, characters are segmented, and recognized to determine the identity of the vehicles. However, the steps in this workflow will fail to produce expected results in low resolution images and in a less constrained environment. Thus in this work, several improvements are made to this ANPR workflow by incorporating intelligent heuristics, image processing techniques and domain knowledge to build an ANPR system that is capable of identifying vehicles even in low resolution video frames. Main advantages of our system are that it is able to operate in real-time, does not rely on special hardware, and not constrained by environmental conditions. Low quality surveillance video data acquired from a toll system is used to evaluate the performance of our system. We were able to obtain more than 90% plate level recognition accuracy. The experiments with this dataset have shown that the system is robust to variations in illumination, view point, and scale. |
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