Moving vehicle velocity estimation from obscure falling snow scenes based on brightness and contrast model

The paper presents a moving vehicle velocity estimator for interpreting scenes under bad weather conditions. The estimator is based on the use of a non-linear robust velocity estimator with a contrast and a brightness variation model. This model is robust against falling-snow patterns that exhibit properties of infinite shapes and size. In this model, no prior knowledge of white color is applied because both snowfalls and vehicles may have the same color. Instead, the method is to minimize the objective function of the model with four variables: horizontal and vertical velocity, brightness, and contrast. Two velocity components of moving vehicles are accurately detected because the falling snow patterns are captured in the brightness and the contrast images. A simple scene interpretation is examined with a modified clustering algorithm. To verify the effectiveness of the method, the recognition rate is compared with that of a conventional velocity detection method.