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Research on moving target detection algorithm based on MRA and wavelet threshold

机译：基于MRA和小波阈值的运动目标检测算法研究

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Aiming at solving the noise problem which had lowered the accuracy in the target detection result from the process of image-collection and image-transmission, the paper proposes a new target detection algorithm based on the improved wavelet threshold. Firstly, these images are filtered by a denoising method, which combines the wavelet threshold method with the correlation of the wavelet coefficients, and its “Zoom” feature can eliminate the negative impact of noise; then a combined algorithm which connects background subtraction and two consecutive inter-frame subtraction is set up in order to combine their advantages and improve the effect of target detection. Compared simulation results of the four models, the results show that the target detection method based on multi-scale wavelet threshold is reasonable and effective, and more suitable for the real-time target detection.
机译：为了解决降低图像采集和传输过程中目标检测结果精度的噪声问题，提出了一种基于改进的小波阈值的目标检测算法。首先，这些图像通过去噪方法进行滤波，将小波阈值方法与小波系数的相关性结合起来，其“缩放”功能可以消除噪声的负面影响。然后建立了一种将背景减法和两个连续的帧间减法相结合的组合算法，以结合各自的优势，提高目标检测的效果。通过对四种模型的仿真结果比较，结果表明基于多尺度小波阈值的目标检测方法是合理有效的，更适合于实时目标检测。

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