Moving object detecting and tracing method in complex scene

Abstract

The present invention discloses method for moving target detection and tracking in a complex scene. The method comprises two steps of multiple moving target detection and multiple moving target tracking: in the multiple moving target detection, a background model based on self adapting nonparametric kernel density estimation is established with the aim at the monitoring of the complex scene, therefore the disturbance of the movement of tiny objects can be effectively suppressed, the target shadow is eliminated, and the multiple moving target is detected; in the multiple moving target tracking, the target model is established, the moving state of the target is confirmed through ''matching matrix'', and corresponding tracking strategy is adopted according to the different movement conditionof the target. Target information is ''recovered'' through the probabilistic reasoning method, and the target screening degree of the target is analyzed with the aim at the problem that multiple targets screen mutually. The algorithm of the present invention can well realize the moving target tracking, obtains the trace of the moving target, and has good real time and ability of adapting to the environmental variation. The present invention has wide application range and high accuracy, therefore being a core method for intelligent vision monitoring with versatility.

Description

A Moving Object Detection and Tracking Method in Complex Scenes technical field The invention mainly relates to the field of intelligent visual monitoring, in particular to a moving target detection and tracking method in complex scenes. Background technique The vision processing system uses image sensors to observe moving objects (such as pedestrians, vehicles, etc.) in the monitoring scene in real time. Describe the behavior of the targets individually and with each other. Visual surveillance technology appeared in the 1960s, and the development of video-based surveillance systems started from analog surveillance (CCTV). The robustness and automation of the surveillance system are low. With the development of technologies and equipment required for visual monitoring such as sensors, computer software and hardware, signal processing and communication, the wide application and rapid development of visual monitoring has a solid material foundation. It is possible to use h...

AI Technical Summary

Problems solved by technology

At present, video surveillance systems using cameras as the main sensor are widely used in various occasions such as banks, parking lots, supermarkets, hotels, residential quarters, etc., but the collection, transmission, display and storage of image data in traditional analog surveillance systems Most of them are based on analog signals, requiring manual continuous observation of multiple monitor images, which not only consumes manpower and material resources, but also often causes missed and false alarms, and cannot effectively detect and stop dangerous events in real time, thus greatly reducing the reliability of the system reliability and reliability, video image data is often only used as evidence for post-accident processing and loses its active and real-time characteristics, and it is impossible to achieve early warning of abnormal events

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