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Moving foreground objects detection in complex scenes is a tough job because it requires high recognition accuracy. Adaptive Gaussian mixture model (AGMM) can be used to extract the foreground objects and it shows good performance, however, the detection quality of the foreground objects under complex scenes is not excellent. In this paper, an AGMM and BP neural network hybrid method is proposed, which is used to extract the foreground objects in complex scenes such as, dynamic backgrounds, illumination changes and moving shadows. In this method, an improved BP neural network is used to post-process the images of the foreground objects that are extracted from the AGMM. The neural network has strong robustness by learning the statistical features of the images. Momentum term and adaptive learning rate are added in the BP neural network algorithm to improve the training speed and robustness of the network. The experimental results show that the proposed AGMM and BP neural network hybrid method can extract the complete foreground objects effectively when compared with some other moving objects detection algorithms.

In the research of anomaly detection methods, obtaining a pure background without abnormal pixels can effectively improve the detection performance and reduce the false-alarm rate. Therefore, this paper proposes a spatial density background purification (SDBP) method for hyperspectral anomaly detection. First, a density peak clustering (DP) algorithm is used to calculate the local density of pixels within a single window. Then, the local densities are sorted into descending order and the m pixels that have the highest local density are selected from high to low. Therefore, the potential abnormal pixels in the background can be effectively removed, and a purer background set can be obtained. Finally, the collaborative representation detector (CRD) is employed for anomaly detection. Considering that the neighboring area of each pixel will have homogeneous material pixels, we adopt the double window strategy to improve the above method. The local densities of the pixels between the large window and the small window are calculated, while all pixels are removed from the small window. This makes the background estimation more accurate, reduces the false-alarm rate, and improves the detection performance. Experimental results on three real hyperspectral datasets such as Airport, Beach, and Urban scenes indicate that the detection accuracy of this method outperforms other commonly used anomaly detection methods.

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