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Method and device for weakly supervised video object segmentation

A technology of object segmentation and weak supervision, applied in image analysis, image data processing, instruments, etc., to reduce the cost of manual calibration, avoid incompleteness, and improve performance

Active Publication Date: 2018-11-27
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to reduce the cost of manual calibration, how to avoid the introduction of irrelevant objects, and improve the performance of video object segmentation

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  • Method and device for weakly supervised video object segmentation
  • Method and device for weakly supervised video object segmentation
  • Method and device for weakly supervised video object segmentation

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Embodiment Construction

[0046] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.

[0047] figure 1 It is a schematic flow chart of a weakly supervised video object segmentation method in a preferred embodiment of the present invention, including the following steps:

[0048] S01 constructs a video object segmentation model, and after inputting the first frame of the test video and the test object bounding box in the first frame, pre-trains the video object segmentation model based on an iterative algorithm;

[0049] In this embodiment, manual calibration is used to give the bounding box of the test object instead of a complete image mask, which significantly reduces the required manual ca...

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Abstract

The invention discloses a method for weakly supervised video object segmentation. The method comprises: constructing a video object segmentation model, after a test video first frame and a test objectbounding box are input, and pre-training the video object segmentation model based on an iterative algorithm; tracking the test object bounding box in each frame after the test video first frame; performing pixel level prediction on each frame after the test video first frame, generating an image mask containing context information of a current frame; optimizing the image mask based on a trackingresult, to obtain a final object segmentation calculation result of the current frame. The invention also discloses a device for weakly supervised video object segmentation. The device comprises a weakly supervised video object segmentation pre-training module, a video object tracking module, a video object segmentation test module and a video object segmentation optimization module. The method and the device reduce labor cost required by video object segmentation and improve performance of video object segmentation.

Description

technical field [0001] The invention relates to the field of video processing in the direction of computer vision, in particular to a weakly supervised video object segmentation method and device. Background technique [0002] Video Object Segmentation (VOS) is one of the hot research issues in the field of computer video processing. Video object segmentation can generate an image mask containing the foreground and background information of the object for each frame in the video. The masks generated by the video object segmentation framework have a wide range of applications, such as video editing, autonomous driving, video surveillance, and video coding applications based on video content. At present, mainstream methods in this field include supervised (Supervised VOS), unsupervised (Unsupervised VOS), and semi-supervised (Semi-supervised VOS) video object segmentation frameworks. [0003] The supervised video object segmentation framework assumes human-labeled prior know...

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Application Information

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IPC IPC(8): G06T7/246G06T7/11
CPCG06T2207/20104G06T7/11G06T7/251
Inventor 张宗璞马汝辉华扬宋涛管海兵
Owner SHANGHAI JIAO TONG UNIV