Robust foveal wavelet-based object tracking
In this work, a foveal wavelet-based Mean Shift Tracking Algorithm is presented. The foveal wavelets introduced by Mallat [16] are known by their high capability to precisely characterize the holder regularity of singularities. Therefore, by using the foveal wavelet transform, image features are accurately identified and are well discriminated from noise. These wavelets are used to extract the texture features of the target object. The extracted features are then used to construct a joint color-foveal textures histogram to represent the target object. Once the joint histogram is obtained, it is applied to the mean shift framework in order to track a target object in a video sequence. The experimental results showed that the proposed approach overcomes the traditional mean shift tracking technique as well as other existing tracking algorithms.