Pedestrian clothing attribute recognition method based on depth attitude estimation and multi-feature fusion

A multi-feature fusion and attribute recognition technology, applied in the field of computer vision, can solve problems such as inaccurate label recognition and pixel analysis area deviation

Active Publication Date: 2019-07-19
FUZHOU UNIV
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AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a pedestrian clothing attribute recognition method based on depth attitude estimation and multi-feature fusion, to overcome the defects in the prior art, and to solve the problems of inaccurate label recognition and pixel analysis area deviation under a single analysis method question

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  • Pedestrian clothing attribute recognition method based on depth attitude estimation and multi-feature fusion
  • Pedestrian clothing attribute recognition method based on depth attitude estimation and multi-feature fusion

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Embodiment Construction

[0078] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0079] Such as figure 1 As shown, the present invention provides a pedestrian clothing attribute recognition method based on depth pose estimation and multi-feature fusion. Aiming at the problems that the existing attribute recognition methods have the interference of environmental factors and thus affect the positioning accuracy, a pedestrian attribute recognition method based on pedestrian pose estimation and multi-feature fusion is proposed. This method first selects part of the retrieval results for subsequent attribute recognition through appearance feature matching. Then, through the SSD-based deep human pose estimation method, the foreground area belonging to pedestrians in the image can be effectively located, and the interference of background factors can be better eliminated. Finally, the analysis results of various methods ar...

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Abstract

The invention relates to a pedestrian clothing attribute recognition method based on depth attitude estimation and multi-feature fusion. The method comprises the following steps: firstly, selecting apart of retrieval results for subsequent attribute recognition through appearance feature matching; then, through an SSD-based depth human body posture estimation method, a foreground region belongingto a pedestrian in the image can be effectively positioned, and background factor interference can be well eliminated; and finally, fusing analysis results of multiple modes, and combining an iterative smoothing process to enhance the correlation between an attribute tag and a pixel by adopting a maximum posterior probability distribution mode to obtain a final attribute analysis recognition result. According to the method, the problems of inaccurate label recognition, pixel analysis area deviation and the like under a single analysis mode are solved. The method is simple and flexible, and has high practical applicability.

Description

technical field [0001] The invention belongs to the fields of computer vision, deep learning, and image processing, and is applied to scenarios such as intelligent monitoring and pedestrian re-identification, and is specifically a pedestrian clothing attribute recognition method based on depth attitude estimation and multi-feature fusion. Background technique [0002] Pedestrian attribute recognition in surveillance images acquired from surveillance video is challenging in the real world for the following reasons: (1) the imaging quality is poor, usually low resolution, and easily affected by motion blur; (2) attributes may Because of the influence of the appearance of the clothes worn or worn by pedestrians, and because of the different postures of pedestrians in different images, the corresponding attributes are located in different spatial positions in the image; (3) It is difficult to collect tagged attribute data from surveillance video images And only available in smal...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/62G06N3/04G06T5/00
CPCG06T5/002G06V10/464G06N3/045G06F18/23213G06F18/214G06F18/24143
Inventor 柯逍李振达
Owner FUZHOU UNIV
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