A Long-term Object Tracking Method Based on Width Learning

Abstract

The invention discloses a long-term target tracking method based on width learning, comprising the following steps: establishing a width learning system; tracking based on the width learning system and a full-image detection mechanism based on an accelerated robust feature algorithm. The present invention is based on the long-term target tracking of the breadth learning system, and the breadth learning architecture is relatively shallow and has low requirements for computing resources so that it can be deployed on low-end devices without losing too much accuracy. The invention obtains a target tracking model with fast training speed, low reconstruction cost and greatly reduced time cost, and has great advantages in detecting deformation, rotation and occlusion in the process of target tracking. Since the present invention applies the full-image detection mechanism based on the SURF algorithm, when the target is completely occluded and the width learning system judges that the target is lost, when the target reappears, it can quickly obtain target information and update the target position, making the tracking effect more stable. Robust and reliable.

Description

technical field [0001] The invention relates to target tracking in the technical field of computer vision, in particular to a long-term target tracking method based on width learning. Background technique [0002] Target tracking has a very wide range of research and applications in many fields such as visual navigation, behavior recognition, intelligent transportation, environmental monitoring, battlefield reconnaissance, and military strikes. At present, the classic tracking method has poor adaptability to target scaling, rotation, occlusion, etc.; the more popular research is represented by the scale-invariant feature transformation method, namely the SIFT algorithm. The SIFT algorithm calculates the Gaussian filter of different windows at multiple scales. Image processing is used to achieve robustness to multi-scale scaling, rotation, blurring, etc. of the target, but it has a large amount of calculation and high complexity, and it is difficult to meet the real-time proc...

AI Technical Summary

Problems solved by technology

At present, the classic tracking method has poor adaptability to target scaling, rotation, occlusion, etc.; the more popular research is represented by the scale-invariant feature transformation method, namely the SIFT algorithm. The SIFT algorithm calculates the Gaussian filter of different windows at multiple scales. Image processing is used to achieve robustness to multi-scale scaling, rotation, blurring, etc. of the target, but it has a large amount of calculation and high complexity, and it is difficult to meet the real-time processing requirements; while the mean shift theory uses the histogram as a feature and uses the kernel probability density to estimate The method to achieve target tracking, although it is robust to occlusion and rotation of the target, but it is not effective for large-angle rotation and hyperplane rotation; the target based on filter theory represented by particle filter and Kalman filter The tracking method takes filtering prediction as the core idea, and it is very robust to partial and complete occlusion of the target. However, there are still many problems in target tracking, such as large-scale scaling, rotation, hyperplane rotation, illumination changes, and partial occlusion of the target. , blurring, reacquisition after the target disappears from the field of view, etc.

In short, there is no good and complete solution at present. With the application of deep learning in the image field, the accuracy and real-time performance of target tracking have been greatly improved, and the shortcomings of general filtering algorithms have also been overcome. Improve

However, the deep learning network is complex, the training period is long, its construction and update process takes a long time, the amount of calculation is large, and the real-time tracking needs to be strengthened

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