The aim of this paper is to proposes methods for better recognizing and positioning ships sailing in critical and wide-area waterways during monitoring operation.

Based on video surveillance technology, the joint use of the motion and appearance features of ship target is carried out to realize a wide-area multi-dimensional recognition function via the combination of background subtraction based moving object detection algorithm and deep learning based target recognition algorithm. In addition, the improved approaches including water ripple noise reduction, hierarchical moving object detection and window segmentation of waterway monitoring image are put forward to further improve recognition accuracy.

The field demonstration results show that the improved methods proposed in this paper allow the accurate recognition of a ship of any type or size on the monitoring screen, and the use of conventional cameras can also achieve the recognition and position of a ship navigating a water area within a radius of 3 km.

The improved methods proposed in this study have a range of advantages including wide-area monitoring, complete coverage of ship types and sizes, automatic target recognition and robust anti-interference ability.