Method for detecting reaction type object

Abstract

The invention discloses a method for detecting a reaction type object, belonging to the field of image processing and comprising the following steps: performing an object slicing program, wherein foreground objects and slicing data are determined by first image data and target positions; performing an object capturing program to enable each foreground object to have corresponding first characteristic data; performing an object tracing program to obtain at least one second characteristic datum; performing an object projection program to predict the target positions which correspond to the foreground objects according to the second characteristic data and second image data, and outputting the target positions to the object slicing program to perform the object slicing of third image data. The invention is suitable for processing images.

Description

technical field [0001] The invention relates to image processing, in particular to an object detection method for predicting the position of a foreground object for object segmentation. Background technique [0002] In the image processing system, if the image processing system can process objects instead of each pixel, then the image processing system can obtain more information about the content of the frame, and can further process events occurring in the frame. By using the characteristics of the objects in the frame, such as new appearance, movement, color, etc., the object detection algorithm in the prior art can detect the foreground objects and segment the image into foreground and background objects. Object detection algorithms can be applied in many fields, such as: intelligent security surveillance systems, computer vision applications, man-machine communication interfaces, image compression, etc. [0003] For intelligent security monitoring systems, object detec...

AI Technical Summary

Problems solved by technology

[0011] The accuracy of the object cutting algorithm in the prior art has not yet reached a satisfactory level, so there are many limitations in application, such as:

[0012] 1. When the object and the background color feature are quite close, the existing object cutting algorithm is not easy to cut accurately

[0013] 2. The existing object cutting algorithm is prone to the phenomenon that the object is disconnected due to inadvertent cutting (such as: a part of the body is similar to the background color), and then a single object is judged as two objects

[0014] 3. When there are light reflections and shadow changes in the picture, the existing object cutting algorithm is not easy to cut accurately, but it is easy to cut out the light and shadow changes as new foreground objects, which increases the number of false alarms

When the object learning rate is slow, if the background changes, the background model cannot be updated in real time

These effects will cause the failure of the object segmentation algorithm

[0016] To sum up the above, the existing object segmentation algorithm not only has many limitations, but also has many serious shortcomings, which makes the image processing process produce many flaws.

Most of these shortcomings are caused by the fact that the existing object segmentation algorithms all use pixels as the starting point. Therefore, the existing object segmentation algorithms need to be improved urgently.

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