Moving shadow detection by integrating multiple features

To segment moving objects from their shadows in video surveillance systems, a GMM and MRF-based moving cast shadows detection and removing method was proposed. First, the Gaussian mixture model was adopted to build statistical models to describe background, and the foreground pixels were obtained by background subtraction method. Second, the feature information of color, edge, texture and spatiotemporal coherence between the foreground pixel area and the corresponding background area were integrated into Markov random fields' energy function. Graph Cut algorithm was used to minimize the energy function, and the final segmentation result was got. Finally, the effectiveness of the method on different video sequences of indoor and outdoor scenes was verified. Experiment results demonstrated that, compared with previous methods, the algorithm could detect and remove moving cast shadows more accurately, reliably and robustly.