Moving target classification method based on on-line study

Abstract

The invention relates to a method which automatically classifies motion targets learning online, models an image sequence background and detects the motion targets, scene variation, coverage viewing angle and partitioning scene, extracts and clusters characteristic vectors, and marks region classes; the number of the motion targets in a sub-region and certain threshold value initialize Gaussian distribution and prior probability to accomplish initialization of a classifier in accordance with the characteristic vectors of all the motion target regions that pass through the sub-region; the motion targets in the sub-region are classified and parameters of the classifier are online iterated and optimized; classification results in the process of tracking the motion targets are synthesized to output the classification result of the motion result that learns online. The invention is used for detection of abnormalities in monitor scenes, establishing rules for various class targets, enhancing security of monitor system, identifying objects in the monitor scenes, lessening complexity of identification algorithm, improving rate of identification, and for semantized comprehension for the monitor scenes, identifying classes of the motion target and aiding to comprehension for behavior events occurring in the scenes.

Description

technical field [0001] The invention belongs to the field of pattern recognition, relates to technologies such as image processing and computer vision, and in particular relates to a moving target classification method for 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 general...

AI Technical Summary

Problems solved by technology

However, such algorithms often need to establish a large sample database including different conditions and scenarios, and at the same time need to manually mark the data in the database, which brings a huge workload and a lot of inconvenience

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