Camera self-calibration method based on movement target image and movement information

Abstract

The invention relates to a self-calibration method for a pickup camera based on an image and motion information of a moving target in a video, which comprises: carrying out prospect detection for the video containing the moving target, and extracting moving target regions; extracting characteristics from each moving target region; roughly classifying the moving target regions; extracting mutually vertical three vanishing points from the images and the motion information of the massive moving target regions; and combining height information of the pickup camera to finish full calibration of the pickup camera for monitoring a scene. The method replaces workload and error of manual calibration, and is used for obtaining an actual point distance of a three-dimensional world through point distance in the images and obtaining an actual line included angle of the three-dimensional world through a line included angle in the images based on measurement of the images or the video, used for monitoring object classification and recognition in the scene and compensating inherent perspective deformation of two-dimensional image characteristics, and used for monitoring object recognition in the scene based on a three-dimensional model to obtain a three-dimensional posture and a track and effectively help a system to comprehend behaviors in the scene.

Description

technical field [0001] The invention belongs to the field of pattern recognition, relates to technologies such as image processing and computer vision, and particularly relates to intelligent visual monitoring. Background technique [0002] With the development of technology and the gradual reduction of the price of hardware equipment, a large number of surveillance cameras have been installed in various places, especially those places that are sensitive to security requirements, such as airports, communities, banks, parking lots, military bases, etc. Visual monitoring of dynamic scenes is a frontier research direction that has attracted much attention in recent years. It detects, recognizes, tracks targets and understands their behavior from image sequences captured by cameras. Although surveillance cameras, which are the extension of human vision, are widely used in commercial applications, the current surveillance systems can only record video for later evidence collectio...

AI Technical Summary

Problems solved by technology

[0005] In view of the fact that camera calibration in the prior art often requires manual measurement and point correspondence, which brings a huge workload and affects the promotion of monitoring algorithms in different

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