Scale adaptive multi-attitude face tracking method based on compressive tracking algorithm

Abstract

The invention discloses a scale adaptive multi-attitude face tracking method based on a compressive tracking algorithm. A target face is roughly positioned by using the compressive tracking algorithm so that the search range of a face detection algorithm can be reduced and the accuracy and the real-time performance of the target face detection algorithm can be enhanced; accurate positioning of the target face is realized by using the face detection algorithm, and scale adaptive tracking of the target face is also realized; the problem of tracking failure caused by the fact that the target face leaves the lens and then enters the lens again can be solved by using the target face detection algorithm; and the problem of tracking continuity under the condition of detection failure of the target face detection algorithm can be realized by using the time continuity of the target face movement process. With application of the method, accurate and effective multi-attitude scale adaptive tracking of the camera for the target face can be guaranteed so that the method can be widely applied to video monitoring, human-computer interaction, virtual reality and various security systems like an ATM monitoring entrance guard system.

Description

technical field [0001] The invention relates to the field of image pattern recognition, in particular to a scale adaptive face tracking algorithm based on a compression tracking algorithm. Background technique [0002] In recent years, researchers have made great progress in face tracking technology. In specific occasions such as online teaching, video conferencing, monitoring and monitoring, it is necessary to track, data transfer and analyze the target face in real time. Distance learning, video communication, videophone, identity verification, human-computer interaction and other aspects are closely related to face tracking. [0003] At present, there are many classic face tracking algorithms, such as Camshift tracking algorithm, particle filter method based on sequence Monte Carlo, Mean shift algorithm, etc. Although these algorithms can accurately track the target face, however, Tracking is prone to failure when the pose of the target face changes. [0004] In order ...

AI Technical Summary

Problems solved by technology

The CT algorithm solves the problem of real-time tracking of multi-pose faces, and has the advantages of small amount of calculation, fast tracking speed, and strong real-time performance. However, in the process of tracking the target, there is a problem that the scale cannot be adaptive. question

At the same time, when the target leaves the lens and enters again, the tracking will be invalid

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