Moving object detection based on improved ViBe algorithm

Moving object detection and recognition has been widely used in computer vision and remote sensing field. When the foreground object exists in the initial frame, the original ViBe algorithm has ghost phenomenon and the fixed threshold is not always appropriate for different background complexity. In the light of this, an improved ViBe algorithm is proposed in this paper. In order to reduce the repetition rate of the pixel value in background model, the proposed method changes the way of neighborhood selection so as to improve the accuracy of background model initialization. During the background model update process, different time subsampling factors are used to speed up the update. Based on the characteristic of less texture information in ghost regions, texture feature operators are used to further remove ghost. In addition, the adaptive threshold is used to replace the fixed threshold to improve the anti-noise performance of the algorithm. Shadow features, the unique brightness, hue and saturation, are used to solve the problem that the moving shadow causes the decrease of detection accuracy. Experiments have been conducted on the public ChangeDetection.net data set, indicating that the proposed method is superior to original ViBe algorithm, thus the higher detection accuracy can be achieved and ghost phenomenon and moving shadows can be alleviated at the similar detection efficiency.