Moving object detection in the presence of dynamic backgrounds remains a challenging problem in video surveillance. Earlier work established that the background subtraction technique based on a covariance matrix descriptor is effective and robust for dynamic backgrounds. The work proposed herein extends this concept further, using the covariance-matrix descriptor derived from local textural properties, instead of directly computing from the local image features. The proposed approach models each pixel with a covariance matrix and a mean feature vector and the model is dynamically updated. We made extensive studies with the proposed technique to demonstrate the effectiveness of statistics on local textural properties.