The disclosure relates to an automatic ship tracking method and system based on deep learning network and mean shift, wherein the method includes: collecting surveillance video data which includes collecting coastal region surveillance video data under visible light and extracting each frame of image; performing preprocessing to extract a positive sample and a negative sample of a ship target; inputting the samples of the ship target in the video into a neural network to train a model by a region-based convolutional neural network method; extracting initial frame data of the video and performing ship detection and probability density calculation on initial moment data according to the trained model; and determining a ship tracking result at the current moment by a calculation result of a previous moment. The disclosure has a great detection result for complex scenes such as cloudy, foggy, overcast and rainy days and the like; the method has the advantages of high robustness, better stability and fully automated tracking process; moreover, the stability and accuracy of the neural network method eliminate errors for a mean shift tracking method; and lay a foundation for tracking an emerging target.
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