An Algorithm of Detecting Moving Foreground Based on an Improved Gaussian Mixture Model.

Objective: The technique of detecting moving regions has been playing an important role in computer vision and intelligent surveillance. Gaussian mixture models provide an advanced modeling approach for us. Although this method is very effective, it is not robust when there are some lighting changes and shadows in the scenes. This paper proposes a mixture model based on gradient images. Method: We firstly calculate gradient images of a video stream using the Scharr operator. We then mix RGB and gradient, and use a morphological approach to remove noise and connect moving regions. To further reduce false detection, we make an AND operation between two modeling results and result in final moving regions. Result: Finally, we use three video streams for analysis and comparison. Experiments show that this method has effectively avoided false detection regions resulting from lighting change and shadow, and improves the accuracy of detection. Conclusion: The approach demonstrates its promising characteristics and is more applicable in real-time detection.