The present invention discloses a twin network target tracking
algorithm with enhanced positioning
quality information, including: determining the target, obtaining the first frame image, determining the
annotation box using known information,
cropping the target area, and obtaining the target template; similarity comparison, obtaining the current frame, using the
feature extraction subnetwork to obtain the feature map of the target and the search area and perform similarity comparison to obtain a
similarity matrix; target positioning, the fusion classification
branch performs the classification task based on the
similarity matrix, and outputs the similarity
prediction score; boundary prediction, the regression
branch predicts the target boundary based on the
similarity matrix, and selects the best prediction box in combination with the similarity
prediction score; outputs the tracking result, outputs the target boundary box, and performs target tracking for the next frame. The present invention can improve the classification and boundary prediction process, introduces the CNN network to make the model more fully utilize the
semantic information in the feature map, and the prediction result has a higher credibility.