Human Appearance Matching Based on Major Color and Spatio-Texture Features Across Disjoint Camera Views

Human tracking across disjoint camera views has obtained extensive concern with the increasing use of multi-camera surveillance with non-overlapping camera views. Human appearance matching is the key to continuous human tracking. Accurate human appearance matching among disjoint camera views is difficult due to various factors such as different view angles and illumination change. In this paper a method incorporating major color and spatio-texture features is proposed. Both features used are robust to illumination change. Major color describing global information is robust to the changes in view angles. A subtractive clustering method is used to extract major color representations from each object and then a formulation based on the normalized RGB color distance is proposed to weight the similarity among different major colors. The major color matching is calculated in an anniversary way. For objects owning similar color information, a spatio-texture feature concerning detailed information is exploited for further matching. In this paper, canny operators extracted from selected area are used as textural information. We test our approach indoor, and the results show that this method has successfully achieved a high matching accuracy in spite of general illumination change.