Method and device for tracking mixing features

Abstract

The invention discloses a method for tracking mixing features. Feature points of key frame images are extracted and saved by SIFT (scale-invariant feature transform) algorithm. A KLT (Kanade-Lucas-Tomasi feature tracker) algorithm thread tracks the feature points of the key frame images by KLT algorithm to obtain the feature points of current frame images, detects whether number of the feature points is sufficient or not for the next tracking after homography is successfully calculated, and notifies a SIFT algorithm thread when the feature points are insufficient. The SIFT algorithm thread extracts and matches the feature points according to the notice given by the KLT algorithm thread, and returns the matched feature points to the KLT algorithm thread. The KLT algorithm thread blends the received feature points returned by the SIFT algorithm thread into tracking feature points remained in the KLT algorithm thread for tracking. The invention further discloses a device for tracking mixing features. By the method and the device, stable tracking is achieved. The method and the device are applicable to camera tracking fields such as virtual studios and virtual sports.

Description

technical field [0001] The invention relates to computer vision technology, in particular to a tracking method and device for mixed features. Background technique [0002] In computer vision technology, the stability and real-time performance of camera tracking are the most challenging problems. Generally speaking, the entire tracking system can be divided into two types: matching type and tracking type. The matching tracking process includes three parts: feature point extraction, feature point matching, and camera pose calculation; the tracking track process includes: feature point extraction, feature point tracking, and camera pose calculation. [0003] In the matching scheme, the scale-invariant feature transform (SIFT) algorithm proposed by David Lowe in 2004 is a typical representative. The SIFT algorithm requires first taking an image as a key frame, using the DoG algorithm to extract a certain number of feature points from the key frame, and then using a method of m...

AI Technical Summary

Problems solved by technology

[0005] The above-mentioned SIFT algorithm can obtain stable matching points, but its operation efficiency is low, and it is difficult to apply in real-time systems. Although the KLT tracking algorithm can operate quickly, its stability cannot be guaranteed, especially when the tracking is lost, it cannot automatically retrieve features. point, tracking stability cannot be guaranteed in natural scenes

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