Research On Ship Target Tracking Algorithm Based On Graph Matching

Target detection and tracking technology is one of the key technologies with research significance and application value in the field of computer vision.Target tracking refers to the determination of the target to be tracked in subsequent frames by processing a sequential sequence of frames on the basis of target detection task.Detecting and tracking ship targets is an application of target tracking in specific scenarios.It has important application value in military defense and production and life.Visible light images and video information often have high resolution,contain rich feature information,and have strong real-time performance.Realtime monitoring of sea targets can be achieved by processing such video images.In the past,target detection and tracking often used classic algorithms such as particle filtering.In recent years,deep learning-based target detection and tracking methods have also made breakthroughs.However,designing a target tracking system under the sea background requires the system to maintain accuracy and real-time performance.Under the premise,it has good robustness to influencing factors such as sea wave background and light weather.Therefore,based on the graph structure and graph matching algorithm,this paper proposes two new ship target detection and tracking methods under the background of visible light on the sea surface.The main contents of this paper are as follows:1.Propose a target detection and tracking system based on the Hungarian graph matching algorithm and Kalman filter.Specifically,the algorithm converts continuous frame images of a detection network such as YOLOv3 into graph structure data as input information,and expresses the position information between the target and the target as points and lines in the figure.Then use the Kalman filter algorithm to predict the position of the target in the current frame in the next frame,and then use the metric method of the fusion of apparent features and motion features to determine the maximum match between the graph composed of the current frame and the graph composed of the next frame.,So as to achieve the purpose of tracking the target.For ships doing nonlinear motion,this chapter proposes to replace the traditional Kalman filter system with an unscented Kalman filter system based on UT transformation.Experimental data shows that this method effectively improves the performance of ships doing nonlinear motion.Tracking effect.2.Propose a ship target tracking method based on graph neural network.This method converts the pre-training data set into the node set,edge set and global vector of the graph structure,obtains the appearance similarity matrix and the motion similarity matrix through the neural network,and then uses the optimization algorithm to train the graph neural network according to the obtained feature matrix,and update Setting parameters of the network.Then use the trained graph neural network to process the data set to obtain the similarity matrix of the object to be detected,and finally use the matching algorithm to calculate the similarity matrix to obtain the matching result of the target in the data set.Quantitative and qualitative experimental analysis proved the effectiveness of the method.