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Excavator working state identification method based on hybrid LBF shape regression model

A technology of working state and regression model, applied in character and pattern recognition, computer components, instruments, etc., can solve the problem of less research on LBF shape regression model detection

Active Publication Date: 2017-05-10
SOUTH CHINA AGRI UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

However, there are very few studies on the detection of feature points of other target objects using the LBF shape regression model.

Method used

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  • Excavator working state identification method based on hybrid LBF shape regression model
  • Excavator working state identification method based on hybrid LBF shape regression model
  • Excavator working state identification method based on hybrid LBF shape regression model

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Embodiment Construction

[0022] The present invention will be further described below in conjunction with the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0023] Such as figure 1 , the present invention is divided into off-line learning stage and on-line recognition working stage:

[0024] S1, during the learning phase, prepare for excavator training, train the hybrid LBF shape regression model of the excavator, use the shape feature to calculate the change angle, construct the feature descriptor MMF (Machine Motion Feature) of the working state of the excavator, and train the MMF as input SVM classifier for excavator working state recognition.

[0025] S11: Excavator dataset preparation.

[0026] In the experiment, the DPM (deformable part model) detection model is used to detect the excavator in the video sequence, and the detected 3000 excavator image sequences are saved as the material of this experiment. For each excavator image, manually mark ...

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Abstract

The invention discloses an excavator working state identification method based on a hybrid LBF (Local Binary Features) shape regression model. The method comprises the following steps: 1) training a hybrid LBF shape regression model of an excavator, and using the model to predict the shape (i.e., a set of relative coordinates of feature points) of the excavator input to a video frame; 2) calculating working state feature descriptors of the excavator according to the coordinates of the feature points and the detected length-width ratio of the excavator; and 3) utilizing a SVM classifier to determine the current state (that is to say, a working state or a non-working state) of the excavator. The excavator working state identification method accurately and automatically identifies the working state of the excavator on a land, and provides an intelligent means for on-site construction monitoring of a construction site.

Description

technical field [0001] The present invention relates to the technical field of intelligent video analysis, and more specifically, relates to an excavator working state recognition method based on a hybrid LBF (Local Binary Features, local binary features) shape regression model. Background technique [0002] my country's land resources are becoming more and more serious, and various illegal land use cases are also frequently occurring. The Ministry of Land and Resources attaches great importance to land law enforcement and supervision. In 2011, the Ministry of Land and Resources carried out land video surveillance pilots in 15 prefecture-level cities and counties (cities, districts), and carried out video surveillance on key areas prone to illegal land use. Excavators are one of the most important construction machinery in engineering construction. Accurate and automatic identification of the working status of excavators between land is an important means to timely discover ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/42G06V10/467G06V10/44G06F18/2411
Inventor 薛月菊毛亮林焕凯
Owner SOUTH CHINA AGRI UNIV
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