Recurrent neural network attention model-based pedestrian attribute recognition network and technology

A technology of cyclic neural network and attention model, which is applied in the field of neural network and image recognition, can solve the problem of ignoring the spatial locality of attributes in the semantic connection between pedestrian attributes, and achieve the effect of high recognition accuracy of pedestrian attributes

Active Publication Date: 2018-11-30
TSINGHUA UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, this method only considers the semantic connection among pedestrian attributes but ignores the spatial locality of attributes

Method used

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  • Recurrent neural network attention model-based pedestrian attribute recognition network and technology
  • Recurrent neural network attention model-based pedestrian attribute recognition network and technology
  • Recurrent neural network attention model-based pedestrian attribute recognition network and technology

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Embodiment 1

[0037] A pedestrian attribute recognition network based on a recurrent neural network attention model, such as figure 1 shown, including:

[0038] Using the original full-body image of the pedestrian as an input to extract the first convolutional neural network of the feature N(x) of the whole-body image of the pedestrian;

[0039] Using the pedestrian full-body image feature N(x) as the first input, the attention heat map A of the attribute group concerned at the last moment t-1 (x) As the second input, output the attention heat map A of the attribute group concerned at the current moment t (x) and pedestrian features H after local highlighting t (x) recurrent neural network;

[0040] Using the partially highlighted pedestrian feature H t (x) As input, a second convolutional neural network that outputs the predicted probability of the attribute of the current group of interest.

[0041] In the pedestrian attribute recognition network provided in this embodiment, the part...

Embodiment 2

[0053] A pedestrian attribute recognition technology based on a recurrent neural network attention model, including:

[0054] S1. Obtain a certain number of pedestrian images with attributes to be identified, and mark whether the images have certain or certain attributes, and obtain a data set that can be used to train the recognition effect of pedestrian attributes; then filter all the attributes marked, Then the filtered attributes are grouped according to the semantic and spatial neighbor relationships;

[0055] S2. Using the combination of the Inception network and the convolutional cyclic neural network, construct a pedestrian attribute recognition network based on the convolutional cyclic neural network attention model, including:

[0056] S2-1. Use the Inception network to extract the original full-body image of the pedestrian to obtain the feature N(x) of the whole-body image of the pedestrian;

[0057] S2-2. At time i, use the pedestrian full-body image feature N(x) ...

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Abstract

The invention provides a recurrent neural network attention model-based pedestrian attribute recognition network and technology. The pedestrian attribute recognition network comprises a first convolutional neural network, a recurrent neural network and a second convolutional neural network, wherein the first convolutional neural network is used for extracting a whole body image feature of a pedestrian by taking an original body image of the pedestrian as an input; the recurrent neural network is used for outputting an attention heat map of an attribute group concerned at the current moment andlocally highlighted pedestrian features by taking the whole body image feature of the pedestrian as a first input and taking an attention heat map of an attribute group concerned at the last moment as a second input; and the second convolutional neural network is used for outputting an attribute prediction probability of the currently concerned group by taking the locally highlighted pedestrian feature as an input. According to the network and technology, a recurrent neural network attention model is utilized to mine an association relationship of pedestrian attribute area space positions soas to highlight positions of areas corresponding to attributes in images, so that higher pedestrian attribute recognition precision is realized.

Description

technical field [0001] The invention belongs to the technical field of neural network and image recognition, and in particular relates to a pedestrian attribute recognition network and technology based on a cyclic neural network attention model. Background technique [0002] Pedestrian attribute recognition technology can help people automatically complete the task of searching for specific people from massive image and video data. However, due to the low image quality of the surveillance video, the small size of the labeled pedestrian attribute data set, and the difficulty in obtaining it, it greatly increases the difficulty of identifying pedestrian attributes from surveillance video images. The existing pedestrian attribute recognition methods based on deep neural network are divided into two categories: convolutional neural network (CNN) method and convolutional neural network combined with recurrent neural network (CNN-RNN). Existing CNN methods such as the DeepMAR met...

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
CPCG06N3/08G06V40/103G06V10/44G06N3/045G06F18/2414G06F18/214
Inventor 丁贵广赵鑫
Owner TSINGHUA UNIV
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