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Online video foreground and background separation method based on regular error modeling

A technology of foreground and background, error modeling, applied in image analysis, image data processing, instruments, etc., can solve the problem of not achieving high precision and high efficiency.

Active Publication Date: 2017-06-09
XI AN JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

Although there are some online separation methods, they often cannot meet the dual requirements of high precision and high efficiency.

Method used

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  • Online video foreground and background separation method based on regular error modeling
  • Online video foreground and background separation method based on regular error modeling
  • Online video foreground and background separation method based on regular error modeling

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Embodiment 1

[0105] Embodiment 1: adopt data set Li Datasets (https: / / fling.seas.upenn.edu / ~xiaowz / dynamic / wordpress / decolor / ) as the computer simulation experiment object of the present invention (see Figure 4 first row). See the following table (the foreground background separation speed of each video application of the present invention in the Li Datasets data set, wherein FPS represents the number of data frames processed per second):

[0106]

[0107] This dataset contains 9 surveillance video datasets, including static background videos, background videos that change with lighting conditions, and dynamic background videos, and some of the data have real annotations of pre-marked foregrounds (see Figure 4 The second column), the frame data of some videos such as figure 2 shown. The present invention extracts 200 frames of data from various videos for experiments. See the process figure 1 :

[0108] Step S1: Obtain Li Datasets video data;

[0109] Step S2: Construct a low-r...

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Abstract

The invention provides an online video foreground and background separation method based on regular error modeling. The method comprises the following steps: 1) obtaining in real time the video data of a monitoring system; 2) based on the real-time change of the video background environment, embedding into the model the transform operator optimization variables based on the video background; 3) based on the characteristics of the video foreground target featuring constant change, constructing a regular error modeling model; 4) combining the step 2 and step 3 for a complete statistical model; according to the maximum posteriori estimation method, obtaining a complete monitoring video foreground and background separation model; 5) performing down sampling to the video data; accelerating the calculation on the video foreground and background separation model of step 4 to realize the real-time solution to the model; and 6) according to the monitoring video data foreground and background separation result obtained from step 5, outputting the foreground and the background in real time. The invention provides a high-speed and high-precision online video foreground and background separation method, which is of great significance in detecting, tracking, identifying and analyzing the target in a monitoring video in practical use.

Description

technical field [0001] The invention relates to a video processing method for monitoring video, in particular to an online video foreground and background separation method based on regular error modeling. Background technique [0002] Surveillance video foreground and background separation has important application value in real life, such as object tracking, urban traffic monitoring and so on. But nowadays, surveillance equipment is spread all over the world, and the daily surveillance video data is extremely large and complex in structure. Under the premise of ensuring high precision and high efficiency, it is still a huge challenge to separate the foreground and background of surveillance video in real time. [0003] In the field of image processing, there are quite a few technologies for foreground and background separation of videos. Common techniques include direct separation methods based on statistical assumptions, subspace learning methods, and online separation ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/194
CPCG06T2207/10016
Inventor 孟德宇雍宏巍岳宗胜赵谦
Owner XI AN JIAOTONG UNIV
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