Compressive sensing-based real-time multi-scale target tracking method

Abstract

The invention discloses a compressive sensing-based real-time multi-scale target tracking method. A sample is modeled by extracting the normalized rectangle features of sampled image, and the normalized rectangle features have higher robustness for the multi-scale target tracking. The normalized rectangle features are very high in dimensionality, so that the method can be used for compressing high-dimensional features based on compressive sensing, the feature vector is compressed under the condition that the extraction scale is not changed, the computation complexity is greatly reduced by integrogram, and the demand of real-time tracking can be met. The compressed feature vector of the sample is classified by a Naive Bayes classifier, so that the most probable position of a target can be determined; the classifier is used for responding and estimating the particle weight and resampling particles so as to prevent the degeneration of particle tracking capability; furthermore, a second-order model is used for estimating and predicting the particle state under the condition that the target movement speed factor is considered. The target in video image can be tracked in real time by the compressive sensing-based real-time multi-scale target tracking method; the method is high in accuracy and low in computation complexity; a tracking frame changes in real time along with the change of target scale, so that the demand of actual tracking application can be met.

Description

technical field [0001] The invention relates to a real-time multi-scale target tracking method based on compressed sensing, which belongs to the technical field of computer vision. Background technique [0002] Video image moving target tracking is one of the most important topics in computer vision. It has a wide range of applications in target supervision, motion detection and recognition, and medical image fields. The task of tracking is the process of estimating the state information of objects in subsequent video frames under the condition that the state of objects in the initial frame of the video is known. Video image moving target tracking is usually described as a dynamic state estimation problem. According to different applications, the state information of the target is generally the kinematic characteristics of the target, such as position coordinates, target scale, etc. Although researchers at home and abroad have proposed various solutions to the problem of v...

AI Technical Summary

Problems solved by technology

Once the target scale changes suddenly, the classifier does not have enough time to learn the changed target features, which will greatly increase the possibility of target loss

Second, the current various discriminative tracking methods often use the time correlation of the target position when collecting samples, select within a fixed radius area, and do not consider the speed and acceleration information of the target movement, and adapt to the fast target moving factor Poor sex

Third, in the current various discriminative tracking methods, the learning parameter values ​​of the classifier are fixed. When the target is covered for a long time, the classifier will inevitably mistake the cover for the target and cause the target to lose track.

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