基于稀疏表示的船体检测方法研究

A new method of vessel detection based on sparse representation

为进一步强化航道安全,解决海事CCTV人工值守、非自动化问题,提出了基于稀疏表示的船体检测方法。利用稀疏表示实现对船体的检测时,首先构建样本特征矩阵,然后利用K-SVD算法对样本特征矩阵进行学习,得到冗余字典,最后对测试样本进行重构,根据马氏距离判断测试样本属性。通过与传统方法的试验比较,实验结果表明,该算法实时性好、检测准确率高,可以很好地对CCTV视频监控的船体进行检测与跟踪,解决CCTV人工值守、非自动化问题,节省大量人力资源。

In order to enhance securities of water-way and improve the effectiveness of the CCTV system, an approach based on sparse representation is proposed to automatically detect and track the vessels on the water-way. First, the dictionary of the samples is constructed by using the K-SVD algorithm to learn and train the samples＇ dictionary, and then the over-complete dictionary that can be used to represent the test samples is obtained. Finally, the mahalanobis distance between the test sample and the reconstructed sample is used to classify the test sample. This method is compared with the traditional methods. The experimental results show that the effectiveness of the vessel detection based on SR outperforms the traditional SVM method in the efficiency and the accuracy, which can solve the vessel detection problem.