Moving object detecting and tracking method based on compressive sensing

Abstract

The invention discloses a moving object detecting and tracking method based on compressive sensing. The moving object detecting and tracking method includes the steps that supporting set fusing and residual compensation sequence rebuilding are carried out on a video sequence, target detection is carried out through background subtraction based on compressive sensing, and a moving object is tracked according to the improved particle filtering algorithm. When background extraction is carried out, the calculated amount of compressive sensing domains will be much less than the calculated amount of spatial domains, so that a large number of storage and technology expenses are saved; meanwhile, the correlation between frames of images in an image sequence serves as prior information and is used in the compressive sensing restructuring procedure, the searching space can be reduced, and the restructuring accuracy of a restructuring algorithm can be improved. The tracking performance of the improved tracking algorithm which fuses the compressive sensing principle and sheltering judgment and is based on dynamic weight section is more accurate under the illumination shadow and sheltering conditions, and the degeneration problem is obviously solved.

Description

technical field [0001] The invention belongs to the field of intelligent video monitoring, and in particular relates to a moving target detection and tracking image processing method integrated with compression sensing technology, which realizes detection and identification of abnormal behavior in input video signals. Background technique [0002] With the increasing attention to security issues in the current society and the rapid development of digital video technology, video surveillance has become a research hotspot in the field of computer vision, and it is constantly advancing towards the direction of intelligence and networking. Intelligent video surveillance realizes the detection, extraction, identification and tracking of targets in dynamic scenes without human intervention, and on this basis completes the understanding of the behavior of moving targets, so as to achieve both daily management functions and Make timely judgments when abnormal situations occur. At p...

AI Technical Summary

Problems solved by technology

Due to the complex calculation of the optical flow method, poor real-time performance, and expensive equipment, it is rarely used in the actual environment; the foreground objects obtained by the inter-frame difference method are prone to holes or overlaps; and the background subtraction is simple to operate and requires a small amount of calculation. Complete moving target information, low complexity and good real-time performance, so the target detection method based on background subtraction has been widely used

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