Adaptive threshold for moving objects detection using gaussian mixture model

Abstract

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.

Otsu’s Image Segmentation Algorithm with Memory‐Based Fruit Fly Optimization Algorithm

“…Aslam et al proposed a fast-recursive algorithm for the 3D OTSU method to reduce the computation time of the 3D OTSU method [8]. Soeleman et al investigated the inefficiency of the multithreshold OTSU algorithm in determining the optimal threshold value, and instead of using the exhaustive method, they proposed a fast algorithm based on the multithreshold OTSU algorithm criterion, which substantially improved the segmentation speed [9]. In addition to the improvement of the OTSU algorithm, other image processing methods have also yielded promising results.…”