Robust License Plate Detection Using Covariance Descriptor in a Neural Network Framework

We present a license plate detection algorithm that employs a novel image descriptor. Instead of using conventional gradient filters and intensity histograms, we compute a covariance matrix of low-level pixel-wise features within a given image window. Unlike the existing approaches, this matrix effectively captures both statistical and spatial properties within the window. We normalize the covariance matrix using local variance scores and restructure the unique coefficients into a feature vector form. Then, we feed these coefficients into a multi-layer neural network. Since no explicit similarity or distance computation is required in this framework, we are able to keep the computational load of the detection process low. To further accelerate the covariance matrix extraction process, we adapt an integral image based data propagation technique. Our extensive analysis shows that the detection process is robust against noise, illumination distortions, and rotation. In addition, the presented method does not require careful fine tuning of the decision boundaries.