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Object attribute recognition method, device, computing device and system

A technology of attribute recognition and recognition method, which is applied in the field of image processing, and can solve problems such as poor robustness, inaccurate pedestrian attribute recognition, standing, some sitting, some riding bicycles, etc.

Active Publication Date: 2022-05-31
HUAWEI TECH CO LTD +1
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  • Claims
  • Application Information

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Problems solved by technology

However, images of pedestrians usually have a variety of poses. For example, some pedestrians in the image are standing, some are sitting, some are riding a bicycle, etc. It is difficult for a rigid deep convolutional neural network to overcome the changes in pedestrian posture. Inaccurate identification of attributes and poor robustness

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  • Object attribute recognition method, device, computing device and system
  • Object attribute recognition method, device, computing device and system
  • Object attribute recognition method, device, computing device and system

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Embodiment Construction

[0039] The following briefly introduces related concepts involved in various embodiments of the present invention:

[0040] A convolutional neural network (CNN) is a deep neural network with a convolutional structure. A convolutional neural network consists of a feature extractor consisting of a convolutional layer and a subsampling layer. The feature extractor can be regarded as a filter, and the convolution process can be regarded as using a trainable filter to convolve with an input image or convolution feature plane (feature map). The convolutional layer refers to the neuron layer that performs convolution processing on the input signal in the convolutional neural network. In the convolutional layer of a convolutional neural network, a neuron can only be connected to some adjacent neurons. A convolutional layer usually contains several feature planes, and each feature plane can be composed of some rectangularly arranged neural units. Neural units of the same feature plane...

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Abstract

The embodiment of the present invention discloses an object attribute recognition method, device, computing device and system. The method includes: the computing device extracts the features of M parts in the first image according to the M posture key points, and obtains the features of M parts Further, input the M part feature maps into the first attribute recognition model to obtain the first attribute recognition result of the target object. Wherein, the first image is the original image or the original feature map extracted according to the original image, the original image includes the target object, the target object includes M parts, the M posture key points correspond to the M parts one by one, and the M parts correspond to the M parts. The M part feature maps correspond one-to-one. In the embodiment of the present invention, before the first attribute recognition model performs attribute recognition on the target object, the first image is disassembled to obtain M part feature maps that have nothing to do with the pose of the target object, so as to overcome the influence of the pose of the target object on the recognition result, so that The attribute recognition of the object is more accurate and robust.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an object attribute recognition method, device, computing device and system. Background technique [0002] With the rapid development of image recognition technology, various recognition technologies are used in many applications, for example, face recognition technology and object attribute recognition technology. Especially in the field of pedestrian-based retrieval, attribute recognition of pedestrians is crucial. [0003] Early pedestrian attribute recognition generally relied on artificially designed features, and then classified based on support vector machines (SVM). However, hand-designed features are difficult to deal with various complex conditions in actual monitoring scenarios and various changes of pedestrians themselves, such as posture and viewing angle. Recent attribute recognition models based on deep convolutional neural networks are usually b...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V40/10G06V10/82G06N3/04
CPCG06N3/04G06F18/00
Inventor 姚春凤冯柏岚黄凯奇陈晓棠李党伟钱莉
Owner HUAWEI TECH CO LTD
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