Target tracking method based on semi-supervised learning and random fern classifier

Abstract

A target tracking method based on semi-supervised learning and a random fern classifier relates to a target tracking method and solves the different problems that an existing target tracking method is large in calculated amount and high in complexity, cannot meet real time processing requirements, is poor in effects on large angle rotation and hyperplane rotation and the like. The target tracking method can solve the difficult problems target scale change, rotation, hyperplane rotation, noise, shading, bonding, camera vibration, dim and the like in scene to form a stable target track and a full-view online model of the target. The tracking method comprises the steps of initializing the online model to generate and standardize positive examples; initializing a detector; training a detection; tracking a short-term tracker; tracking validity evolution; detecting the detector; combining detection and track; and learning and upgrading training sets. The target tracking method is a complete target tracking solution and is widely applied to fields of actual video monitoring, behavior analysis, intelligent transportation, electronic police, precision guidance and the like.

Description

technical field [0001] The invention relates to a target tracking method, in particular to a target tracking method based on semi-supervised learning and a random fern classifier. 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 correlation tracking method has poor adaptability to target scaling, rotation, occlusion, etc.; research on the popular invariant features represented by SIFT, the SIFT algorithm processes images by calculating Gaussian filters in different windows at multiple scales. Realize the robustness of multi-scale scaling, rotation, blurring, etc. of the target, but its computational complexity is large and complex, and it is difficult to meet the real-time processing requirements; while the mean shift theory uses histogram...

AI Technical Summary

Problems solved by technology

[0003] The present invention solves the problem that the existing target tracking method has a large amount of calculation and high complexity, and is difficult to meet the real-time processing requirements. Hyperplane rotation, noise, occlusion, adhesion, camera shake, blur, and recapture after the target disappears in the field of view, providing a target tracking method based on semi-supervised learning and random fern classifier

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