A Long-term Object Tracking Method Based on Width Learning

A target tracking and width technology, applied in the field of target tracking in the field of computer vision technology, can solve the problems of long training period, large amount of calculation, fuzzy recapture, etc., to achieve stable tracking effect, reduced time cost, and fast training speed.
CN108921877BActive Publication Date: 2021-07-16DALIAN MARITIME UNIVERSITY +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
DALIAN MARITIME UNIVERSITY
Publication Date
2021-07-16

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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.
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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...

Claims

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