Method and system for detection of hangover

Abstract

The invention discloses a residue detection method, which comprises the following steps that: foreground images which are different to a background are detected, foreground pixel points are determined, and then the foreground pixel points are characterized; the persistent state that the foreground pixel points are characterized is timed, and the timed pixel points which achieve a pre-set value are extracted; a connected region formed by the extracted pixel points is detected, and is determined to be a target to be analyzed; the motion characteristics of the target is analyzed to determine whether the targets to be analyzed are the residue. The invention also discloses a residue detecting system, which comprises a target detection device, a timing treatment device, a connected region detection device and a target analysis device, wherein, the timing treatment device times the pixel points, and extracts the pixel points, the characterization state persistent time of which achieve the pre-set value, the connected region detection device detects the connected region which is formed by the extracted pixel points, so as to determine the connected region of the targets to be analyzed, and the target analysis device is used to determine whether the targets to be analyzed are the residue. The invention can precisely extract the effective pixel points of the targets, thereby precisely judging whether the detected targets are the residue.

Description

【Technical field】 [0001] The invention relates to image processing, in particular to a method and system for detecting remnants. 【Background technique】 [0002] The detection of remnants is based on the moving object detection technology. The commonly used method in the moving object detection technology is the background subtraction method, mainly by establishing a background model (background image frame), and then subtracting the background image frame from the current image frame to obtain the background image frame. The difference, this difference is the desired goal. The disadvantage of the existing technology is: after the target is detected, it is directly judged whether the target is moving. If the static time is long enough, it is considered as a remnant. However, if the detection is disturbed, if someone passes by the target, it will not be possible to accurately determine whether the target is stationary. This is because the existing technology does not process ...

AI Technical Summary

Problems solved by technology

The disadvantage of the existing technology is: after the target is detected, it is directly judged whether the target is moving. If the static time is long enough, it is considered as a remnant. However, if there is a disturbance in the detection, if someone passes by the target, it will not be possible to accurately determine whether the target is stationary

This is because the existing technology does not process the target pixels after the target is detected. When the target is detected when it is disturbed, the target is incomplete and unreal. In this way, when judging whether it is stationary or moving It is also impossible to be accurate, because the disturbance changes, the measured target changes, and the target itself is likely to be stationary but is mistaken for moving, resulting in the missed detection of stationary remnants

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