Self-adaptive target response-based target tracking method

Abstract

The invention discloses a self-adaptive target response-based target tracking method which is used for mainly solving the tracking problem of tracking failure caused by appearance deformation and illumination change of a target. The method comprises the following implementation steps of: (1) determining an initial position of a to-be-tracked target; (2) calculating the weight of a correlation filter; (3) predicting the position of a target frame; (4) predicting the size of the to-be-tracked target; (5) constructing a self-adaptive target response matrix; (6) updating the weight of a displacement filter; (7) judging whether the current frame image is the last frame image of a to-be-tracked video sequence or not, if yes, executing step (8), otherwise executing the step (3); and (8) finishingthe tracking. The self-adaptive target response-based target tracking method disclosed by the invention has the advantages that by utilizing self-adaptive target response, the weight of the displacement filter can be calculated and the position and the size of the to-be-tracked target can be predicted.

Description

technical field [0001] The invention belongs to the technical field of computers, and further relates to a moving target tracking method based on adaptive target response in the technical field of computer vision. The invention can be used for tracking moving targets in video sequences acquired by video surveillance, robot navigation and positioning, and unmanned aerial vehicles. Background technique [0002] The main task of moving object tracking is to detect the moving object from the continuous video image sequence, and then determine the position of the moving object in each frame of image. With the continuous deepening of people's understanding of the field of computer vision, moving object tracking has been widely used and developed in this field, and there are already a large number of tracking algorithms to realize moving object tracking. However, due to the influence of objective factors such as occlusion, background clutter, appearance deformation, illumination c...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that because the method first automatically adjusts the size of the tracking frame according to the target size change, and then uses the adjusted tracking frame to perform correlation convolution in the KCF algorithm tracking target, resulting in the tracking position of each frame being different. Accurate, causing drift that is difficult to correct. When the target undergoes strong illumination changes and the target moves quickly, it cannot achieve long-term accurate tracking

The disadvantage of this method is that because it is necessary to determine whether the target is seriously occluded, the threshold of the determination value is set too high or too low to make an error in determining whether it is occluded, so that when the target is occluded or the target is moving quickly, accurate tracking cannot be achieved.

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