Anti-shelter target trajectory predicting and tracking method

Abstract

The invention relates to the technical field of computer vision and pattern recognition, and provides an anti-shelter target trajectory predicting and tracking method. The method comprises the following steps of: selecting a target, initializing a Kalman parameter, and calculating a quantification histogram; reading an image, calculating the position and the size of a tracking window, correcting the central position of the target, and setting the central position of an image searching window of a next frame; predicting the position of the target by a trajectory predicting program; solving an occlusion factor; and according to a sheltered situation, selecting a Kalman filter to work, or converting to trajectory predication based on least square support vector machines, namely determining that the tracking fails if the target is not found when a determined frame number is exceeded in a predicting process; and continuing enabling an MeanShif target tracking algorithm and the Kalman filter to track and the like if the target is found. By using the method, the target which reappears after being sheltered by a large area can be tracked accurately; and the method has good real time and anti-jamming capability.

Description

(1) Technical field [0001] The invention belongs to the technical field of computer vision and pattern recognition, in particular to an anti-occlusion target trajectory prediction and tracking method. (2) Background technology [0002] Object tracking is an important branch of computer vision. In application fields such as video surveillance, object recognition, and human-machine interface, it is often necessary to effectively track moving objects in various complex environments. The tracking system is not only required to be able to adapt to the appearance changes of the target due to various motions in real time, but also to be insensitive to the influence of factors such as occlusion and illumination changes in the scene. [0003] The MeanShift target tracking algorithm is a parameter-free estimation method based on density gradients, developed by Fukunaga in 1975 [1] Proposed, Cheng 1995 [2] Bring it into the field of computer vision. In recent years, the MeanShift t...

AI Technical Summary

Problems solved by technology

However, compared to the area of ​​the target area, if the target encounters a large proportion of occlusion, the target position point found by the MeanShift algorithm is inaccurate, and the Kalman filter composed of this position point information is not accurate for the observation vector of the current frame. Correct, if the Kalman filter is still used to predict the possible position of the target in the next frame, the reliability is obviously very low

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