Building an efficient and robust system capable of working in harsh real world conditions represents the ultimate goal of the traffic video surveillance. Despite an evident progress made in the area of statistical background modeling over the last decade or so, moving object detection is still one of the toughest problems in video surveillance, and new approaches are still emerging. Based on our published method for motion detection in the wavelet domain, we propose a novel, wavelet-based method for robust feature extraction and tracking. Hereby, a more efficient approach is proposed that relies on a non-decimated wavelet transformation to achieve both motion segmentation and selection of features for tracking. The use of wavelet transformation for selection of robust features for tracking stems from the persistence of actual edges and corners across the scales of the wavelet transformation. Moreover, the output of the motion detector is used to limit the search space of the feature tracker to those areas where moving objects are found. The results demonstrate a stable and efficient performance of the proposed approach in the domain of traffic video surveillance.