A real-time multi-scale target tracking method based on compressed sensing

Abstract

The invention discloses a real-time multi-scale target tracking method based on compressed sensing. The sample is modeled by extracting the normalized rectangular feature of the sampled image, which is robust to multi-scale target tracking. Due to the high dimensionality of normalized rectangular features, the invention compresses high-dimensional features based on compressed sensing, extracts scale-invariant compressed feature vectors and uses integral maps to greatly reduce computational complexity to meet the needs of real-time tracking. Use the naive Bayesian classifier to classify the compressed feature vector of the sample to determine the most likely position of the target, and use the classifier response to estimate the weight of the particles, resample the particles to prevent the degradation of the particle tracking ability, and consider the speed of the target using two factors First-order models estimate and predict particle states. The method of the invention can track the target in the video image in real time, has high accuracy, low computational complexity, and the tracking frame changes with the scale of the target in real time, meeting the needs of actual tracking applications.

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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