MARKOV RANDOM FIELD-BASED METHOD FOR LABELING REMOTE CONTROL TOWER VIDEO TARGET

摘要

A Markov random field-based method for labeling a remote control tower video target, comprising the steps of: 1) establishing a model; 2) using a greedy algorithm to solve a sparse representation of a sequence of consecutive video frames, and obtaining a preliminary estimation of the background; 3) using a recurrent neural network to solve an image segmentation problem, and obtaining a foreground target tracking result and a background estimation; 4) using the nearest neighbor method to establish a correspondence between the positions of target coordinate points in the world coordinate system and automatic dependent surveillance broadcast data, so as to associate label information in the automatic dependent surveillance broadcast data with a video, thus achieving automatic labeling. The described method utilizes a sparse sampling means to reduce a data set of a calculation operation and reduce the complexity of solving a background. By using the background as an input and using a Hopfield network self-optimizing feature, an optimized estimation of a foreground target is automatically formed.