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Driving attitude recognition method based on fusion feature of local deformable component model

A technology of fusion of deformable parts and models, applied in the field of intelligent transportation, can solve the problems of weak recognition robustness, recognition speed defects, inappropriate feedback and early warning, etc., to improve recognition speed and accuracy, improve feedback speed, Strong time-sensitive effect

Active Publication Date: 2019-01-01
SOUTHEAST UNIV
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Problems solved by technology

However, the global-based DPM driving posture recognition method contains a lot of redundant information when processing the driving posture image, the recognition of key body parts is not robust, and has obvious defects in recognition speed, which is not suitable for efficient driving under high-speed driving conditions. Feedback and warning

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  • Driving attitude recognition method based on fusion feature of local deformable component model
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  • Driving attitude recognition method based on fusion feature of local deformable component model

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Embodiment Construction

[0028] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.

[0029] A driving posture recognition method based on fusion features of local deformable part models, comprising the following steps:

[0030] The first step: use the video sensor to obtain the image of the driver's posture, and define the driving posture core area (Driving Posture Core Area, DPCA), which are the driver's head area, torso area and hand area: as figure 1 Shown is a schematic diagram of the division of the local core area of ​​the driving posture, and the ...

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Abstract

The invention discloses a driving attitude recognition method based on the fusion feature of a local deformable component model. The method of the invention comprises the following steps: (1) acquiring a driver posture image by adopting a video sensor and defining a driving posture local core area; (2) using linear discriminant analysis to determine the number of components in the local core region of each driving posture, and constructing the local deformable component model for detecting the local core region of driving posture; (3) the score model of the local deformable component model ofthe driving attitude local core region being constructed respectively, and the result of the model being used as the local eigenvector of the driving attitude local core region; (4) the serial fusionrule being used to construct the fusion eigenvector of the local deformable component model of the driver posture; (5) using the support vector machine based on RBF kernel to recognize the pilot's attitude. The invention can effectively detect and recognize the driving posture of the driver.

Description

Technical field: [0001] The invention relates to a driving posture recognition method based on fusion features of local deformable part models, belonging to the technical field of intelligent transportation. Background technique: [0002] In the field of intelligent traffic monitoring, effective real-time monitoring of drivers' driving behavior is an important measure to avoid traffic risks. Various studies have shown that driver error is the core factor of traffic accidents. Therefore, it is particularly important to detect and identify abnormal driving behaviors and attitudes at high speed and with high robustness. kind of challenge. [0003] Deformable Part Model (DPM) is a target detection model proposed by Felzenszwalb et al. The detection process is based on the window scanning method, and achieves scale invariance by constructing image pyramids. However, the global-based DPM driving posture recognition method contains a lot of redundant information when processing t...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/597G06F18/2411
Inventor 赵池航钱子晨赵敏慧何杰林盛梅
Owner SOUTHEAST UNIV
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