Real-Time Illumination Robust Maneuvering Target Tracking Based on Color Invariance

Illumination change is an important factor that affects the accuracy of tracking. In order to enhance the robustness against illumination changes in directions and intensities of the conventional tracking approaches, this paper proposes to apply color invariance theory to maneuvering target tracking. To depress the influence of the background, at the beginning of the process, we use expectation maximization algorithm based on Gaussian mixture model to extract the objects from the environment. Then, instead of using the gray space or RGB space, frames are transformed to color invariant space based on Kubelka-Munk model, followed by the implementation of conventional tracking approach such as mean shift or particle filtering to track the targets. Experimental results show that mean shift and improved particle filtering with color invariance can achieve 64.1 and 16.0 frames per second correspondingly, which is real-time, and the proposed approach improves the robustness of maneuvering targets tracking greatly.