Moving Object Detection Using Adaptive Blind Update and RGB-D Camera

A novel background subtraction approach using an RGB-D camera and an adaptive blind updating policy is introduced. This method in the initialization creates a model to store background pixels to compare each pixel of the new frame with the model in the same location to identify background pixels. The background-model update presented in this paper uses regular and blind updates which also has different criteria from existing methods. In particular, blind update frequently changes based on the background changes and the speed of moving object. This will allow the scene model to adapt to the changes in the background, detecting the stationary moving object and reducing the ghost phenomenon. In addition, the proposed bootstrapping segmentation and shadow detection are added to the system to improve the accuracy of the algorithm in shadow and depth camouflage scenarios. The proposed method is compared with the original method and the other state of the art algorithms. The experimental results show significant improvement in those videos that stationary object appears. In addition, the benchmark results also indicate strong and stable results compared to the other state of the art algorithms.