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Attribute recognition model training method, pedestrian attribute recognition method, electronic equipment and storage medium

A technology for attribute recognition and model training, applied in the field of image processing, can solve problems such as data difficulties and complex scene sources, achieve the effect of less data collection cost, better pedestrian attribute recognition model, and save a lot of attribute labeling work

Active Publication Date: 2020-10-09
SUZHOU KEDA TECH
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  • Abstract
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  • Application Information

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Problems solved by technology

At present, due to the wide variety of pedestrian attributes and complex scene sources, it is extremely difficult to obtain a large amount of fully labeled data

Method used

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  • Attribute recognition model training method, pedestrian attribute recognition method, electronic equipment and storage medium
  • Attribute recognition model training method, pedestrian attribute recognition method, electronic equipment and storage medium
  • Attribute recognition model training method, pedestrian attribute recognition method, electronic equipment and storage medium

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Embodiment Construction

[0051] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0052] Furthermore, the drawings are merely schematic illustrations of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be impleme...

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Abstract

The invention provides a pedestrian attribute recognition model training method, a pedestrian attribute recognition method, electronic equipment and a storage medium. The pedestrian attribute recognition model training method comprises the following steps: constructing a training data set comprising pedestrian pictures marked with attributes and pedestrian pictures marked with IDs; constructing apedestrian attribute recognition network, wherein the pedestrian attribute recognition network comprises a backbone network, a spatial information branch network and a semantic information branch network, and respectively inputting the output of the backbone network into the spatial information branch network and the semantic information branch network; and training the pedestrian attribute recognition network by using the training data set to obtain a pedestrian attribute recognition model, wherein the pedestrian attribute recognition model is used for recognizing attributes in a picture according to the input picture. According to the method and device provided by the invention, comprehensive and robust information is learned through model training, a large amount of attribute labeling work is omitted, and the good pedestrian attribute recognition model is obtained with low data acquisition cost.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a pedestrian attribute recognition model training method, a recognition method, electronic equipment, and a storage medium. Background technique [0002] Pedestrian attribute recognition is a way to judge the attributes of pedestrians based on pedestrian photos or video screenshots, such as gender, clothing color, clothing style, whether they wear glasses, etc. At present, due to the wide variety of pedestrian attributes and complex scene sources, it is extremely difficult to obtain a large amount of fully labeled data. Most of the data has only some attribute annotations, or even no attribute annotations. Since there is only one mark for pedestrian identity data, and multiple images can be generated during a person walking in the video, the cost of marking pedestrian identity data is lower and the difficulty is less than attribute labeling. [0003] Semi-supervised learning is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/10G06V10/44G06V10/56G06N3/045G06F18/24
Inventor 高毓声晋兆龙付马肖潇
Owner SUZHOU KEDA TECH
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