Quality of dirty: A decision making assessment methodology for automatic license plate recognition under dirtied conditions
Automatic license plate recognition (ALPR) from vehicle images plays an important role in intelligent traffic management. A critical problem is that the cameras can be dirtied such that the captured images are unrecognizable. This paper defines a new concept of Quality of Dirty (QoD) of captured images indicating when the dirtied cameras need to be cleaned. An accurate image QoD metric is proposed based on relevant image features and support vector regression (SVR), and it can handle a tricky issue of differentiating captured imaged containing dirtied vehicles from images captured by a dirtied camera. A subjective assessment to collect ground-truth QoD scores of real captured images has also been conducted. Experiments have demonstrated that the proposed image QoD metric achieves high accuracy when predicting the dirtied degree of cameras in the tunnel environment.