Dynamic human face tracking method

Abstract

The invention discloses a dynamic human face tracking method. The method comprises the steps of S1, tracking a target by using a feature-based tracking algorithm, and when the tracking of the target is lost, going to and executing the step S2; S2, predicting a human face region position after the tracking is lost, establishing a feature point template, matching pixel points of the predicted humanface region position with the current feature point template, initializing an optical flow tracking algorithm by successfully matched feature points, tracking the target, and according to a loss stateof the feature points, updating the feature point template; and S3, when a target position which can be tracked by the feature-based tracking algorithm is restored, returning to and executing the step S1. The method can be suitable for human face tracking under complex conditions of deflection, shielding and the like, and has the advantages of simple implementation method, high tracking efficiency and precision, capability of taking stability and timeliness into account, high environmental adaptability, good anti-interference capability and the like.

Description

technical field [0001] The invention relates to the technical field of target tracking, in particular to a dynamic face tracking method. Background technique [0002] At present, the face tracking algorithm is usually combined with model learning to track the face, or track the target face through a single feature, such as color features, texture features, corner features, etc., where the tracking based on model learning follows the face The movement of the face constantly updates the face model and achieves high accuracy. However, this type of algorithm has high computational complexity and requires high hardware performance of the computing system. For example, the establishment of a complex face model requires powerful hardware computing resources. One solution is to process it through a GPU accelerator card, but the corresponding cost is often high, and it is difficult to apply to large-scale applications. Face tracking based on texture features also has this type of pro...

AI Technical Summary

Problems solved by technology

[0005] 1. Algorithms that are not based on face models are easy to lose tracking when the face and camera lens are deflected or the focal length of the camera equipment changes, and cannot meet complex situations such as deflection, camera focal length changes, and occlusions;

[0006] 2. The complexity of establishing a face model and performing tracking operations is relatively high. When it is applied to a large-scale security monitoring, the actual system computing capability is demanding and the cost is high.

[0007] 3. It is difficult to balance the real-time performance and accuracy of face tracking

This method cannot be applied to situations where the face is deflected or occluded. When the face is deflected to a certain extent, the tracking will be lost.

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