Abandoned object detection method based on computer vision

Abstract

The invention relates to an abandoned object detection method based on computer vision, which mainly includes the following steps: updating motion history timing, and studying and updating a background model; by means of the background model, judging that each pixel is in one of three states: background, abandoned object foreground and non-abandoned object foreground, and carrying out state maintenance timing; extracting a connected region formed by the pixels constantly being in the same state of abandoned object foreground and timed at a preset value, and extracting objects to be analyzed; extracting characteristic expressions from the objects to be analyzed, and accumulating the number of the objects to be analyzed with the similar characteristic expressions within preset time; and if the accumulated value reaches a preset value, then carrying out comprehensive analysis on the motion characteristics of the corresponding objects to be analyzed in order to determine whether the objects to be analyzed are abandoned objects or not. The abandoned object detection method based on computer vision can quickly and accurately locate the corresponding regions of extracted abandoned objects, and has the characteristics of wide application range, high self-adaptability, high real-timeness, low missing rate and high reliability.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a method for detecting remnants based on computer vision. Background technique [0002] Remains detection technology is based on moving object detection. The common technique of moving object detection technology is the background difference method, that is, the current image subtracts the background image frame to obtain the difference image, and the object to be detected is obtained by analyzing the difference in the difference image. Then directly analyze the static state of the detected object, if it has not changed for a long time, it is considered as a remnant. One of the shortcomings of the existing technology is that it does not further distinguish whether the foreground corresponding to the difference in the difference image is a legacy or a non-legacy, which will make the detected object region likely contain a large number of non-legacy foregrounds, resultin...

AI Technical Summary

Problems solved by technology

One of the shortcomings of the existing technology is that it does not further distinguish whether the foreground corresponding to the difference in the difference image is a legacy or a non-legacy, which will make the detected object region likely contain a large number of non-legacy foregrounds, resulting in a legacy Object location and extraction are inaccurate, feature expression is unreasonable, and subsequent analysis fails

Another shortcoming of the existing technology is that the preset background image cannot change with the gradient of the scene. Due to the accumulation of gradient factors such as illumination after long-term operation, the actual background image changes greatly relative to the pre-selected background image, thus making the pre-selected background invalid. , leading to false positives and false negatives

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