Real-time target matching tracking method and system

Abstract

The invention provides a real-time target matching tracking method and a system. According to the method, feature information of pixels contained in each of at least two images which are taken in the same place at similar shooting time is analyzed, so that at least one target area from each image is acquired; and then matching target areas of each target area in the corresponding image according to area information occupied by the target area in the corresponding image are determined, wherein the matching target areas are positioned in other images. The areas, which is occupied by a target, in the images are used as tracking objects, the matching target areas, which are positioned in the other images, of each target area are determined based on the area information occupied by the target area in the corresponding image, existing invariant feature extraction algorithm is omitted, accordingly, compared with the prior art, the method is low in complexity, real-time tracking to the target areas can be realized easily, and the method can be applied to more monitoring places.

Description

technical field [0001] The invention relates to the field of intelligent monitoring, in particular to a real-time target matching and tracking method and system. Background technique [0002] With the continuous development of network technology and computer image processing technology, video-based intelligent monitoring system is widely used in road traffic, community monitoring and other occasions. Among them, the intelligent traffic monitoring system mainly involves technologies such as video background training, moving target detection, moving target matching and tracking, event detection and target recognition, among which the main object is the vehicle, so vehicle matching and tracking technology is the main core of the intelligent traffic monitoring system Technology, its accuracy and real-time performance determine the degree of popularization and application of intelligent transportation systems. In order to achieve a better matching effect, most current vehicle ma...

AI Technical Summary

Problems solved by technology

In order to achieve a better matching effect, most current vehicle matching algorithms first extract invariant features such as translation, rotation, scaling, viewing angle change, brightness change, occlusion, and noise from the detected vehicle object, such as: contour, Histogram, SIFT, etc., and then use a certain distance metric function for matching and tracking. However, the complexity of these feature extraction algorithms that remain unchanged is very high, the calculation speed is slow, and it takes a lot of CPU and memory. For the current computer In terms of processing capacity, it is difficult to support four, eight or even more than ten channels of surveillance video processing at the same time, resulting in many intelligent transportation systems are still in the laboratory research stage, and cannot be widely promoted and applied

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