Abstract
Based on broad learning system (BLS), this paper monitors and identifies the behavior of crew members on ships. The main recognition scenarios include both of on deck and in the cabin, and the main recognition tasks include crew tracking and identification. In this paper, the video data is divided into frames and images are segmented. In the preprocessing of images, the filter is used to enhance the information and reduce the noise of the images. Finally, the recognition model is established by the BLS. In the experiment, we use BP neural network (BPNN) and support vector machine (SVM) as comparisons. The proposed method in this paper has achieved the best results in terms of recognition accuracy and training time.
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