Target tracking method based on ORB characteristics point matching

Abstract

The invention relates to a target tracking method based on ORB characteristics point matching. The target tracking method comprises: adopting an ORB (Oriented FAST and Rotated BRIEF) characteristic point set to represent a target object, and in a tracking process of the target object, iteratively updating the ORB characteristic point set by continuously adding new characteristic points and cutting outliers. A nearest neighborhood search algorithm is used between two continuous frames to find out matched characteristic point pairs, and then a motion transformation model of the tracking target is worked out according to the characteristic point pairs by using method that combines random sampling accordance with a multiple transformation model. The target tracking method based on ORB characteristics point matching is better in robustness and higher in efficiency, when being used for processing scenes with complex appearance change, disorder background, illumination variation and shielded target object.

Description

technical field [0001] The invention relates to the technical field of computer vision tracking, in particular to a target tracking method based on ORB feature point matching. Background technique [0002] Object tracking is an important research topic in the field of computer vision. Based on the rapid development of high-performance computers, the convenience of high-quality and inexpensive cameras, and the growing demand for automated video analysis, human beings have great interest in the exploration of visual object tracking algorithms. In recent years, visual tracking has been widely used in many fields, including motion gesture recognition, pedestrian tracking, intelligent monitoring, human-computer interaction, intelligent traffic detection, and vehicle navigation. With the development of the digital home industry and the improvement of the digital home industry chain, the application of video tracking in smart home monitoring has gradually deepened. On the basis of ...

AI Technical Summary

Problems solved by technology

Second, complex background interference

In scenarios such as traffic detection, vehicle navigation, and pedestrian tracking, object tracking is extremely challenging due to complex background interference

There is a lot of noise in the actual complex environment, and the background is cluttered, and the color of the target object is easily confused with the color of the background

Third, the occlusion problem

Fourth, real-time issues

The first three problems are all about examining the robustness of the target tracking algorithm, and the ability of target tracking to meet the needs of real-time processing is also a difficult problem in the field of target tracking.

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