The synthetic aperture radar (SAR) image preprocessing techniques and their impact on target recognition performance are researched. The performance of SAR target recognition is improved by composing a variety of preprocessing techniques. The preprocessing techniques achieve the effects of suppressing background redundancy and enhancing target characteristics by processing the size and gray distribution of the original SAR image, thereby improving the subsequent target recognition performance. In this study, image cropping, target segmentation, and image enhancement algorithms are used to preprocess the original SAR image, and the target recognition performance is effectively improved by combining the above three preprocessing techniques. On the basis of image enhancement, the monogenic signal is used for feature extraction and then the sparse representation-based classification (SRC) is used to complete the decision. The experiments are conveyed on the moving and stationary target acquisition and recognition (MSTAR) dataset, and the results prove that the combination of multiple preprocessing techniques can effectively improve the SAR target recognition performance.

No abstract

Abnormal event detection has attracted widespread attention due to its importance in video surveillance scenarios. The lack of abnormally labeled samples makes this problem more difficult to solve. A partially supervised learning method only using normal samples to train the detection model for video abnormal event detection and location is proposed. Assuming that the distribution of all normal samples complies to the Gaussian distribution, the abnormal sample will appear with a lower probability in this Gaussian distribution. The method is developed based on the variational autoencoder (VAE), through end-to-end deep learning technology, which constrains the hidden layer representation of the normal sample to a Gaussian distribution. Given the test sample, its hidden layer representation is obtained through the variational autoencoder, which represents the probability of belonging to the Gaussian distribution. It is judged abnormal or not according to the detection threshold. Based on two publicly available datasets, i.e., UCSD dataset and Avenue dataset, the experimental are conducted. The results show that the proposed method achieves 92.3% and 82.1% frame-level AUC at a speed of 571 frames per second on average, which demonstrate the effectiveness and efficiency of our framework compared with other state-of-the-art approaches.

No abstract