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Pedestrian re-recognition method combining posture and attention based on double-flow network

A pedestrian re-identification and attention technology, applied in the field of deep neural network, can solve problems such as unstable lighting conditions and low resolution

Inactive Publication Date: 2020-02-11
HANGZHOU DIANZI UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

However, in the real environment, pedestrian re-identification is still a challenging task due to surveillance cameras working in wide-angle mode, very low resolution and unstable lighting conditions and human body pose changes and occlusions.

Method used

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  • Pedestrian re-recognition method combining posture and attention based on double-flow network
  • Pedestrian re-recognition method combining posture and attention based on double-flow network
  • Pedestrian re-recognition method combining posture and attention based on double-flow network

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Embodiment Construction

[0042] The detailed parameters of the present invention will be further specifically described below.

[0043] Such as figure 1 As shown, the present invention provides a deep neural network framework for pedestrian re-identification.

[0044] Step (1), data preprocessing, feature extraction

[0045] For the input image x', it is preprocessed and scaled to a size of 192×96; then a network stream of the two-stream network is used to calculate their respective feature representations. Here, the attention flow is initialized with the pre-trained weights of ImageNet, the existing GoogleNet's first four-layer network model is used to extract attention features, and then the self-attention mechanism is used to associate global features. Here, the pre-trained weights of the COCO dataset are used for initialization in the pose estimation flow, and the first three stages of the existing OpenPose are used to extract the features of human body parts.

[0046] Step (2), spatial feature...

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Abstract

The invention discloses a pedestrian re-recognition method combining posture and attention based on a double-flow network. The method comprises the following steps of: 1, preprocessing an input imageand inputting the input image into a double-flow network to extract features; and 2, combining middle-layer features and high-layer features and associating global information through an attention mechanism. And 3, fusing the attention flow and the attitude estimation flow through bilinear pooling operation to obtain a final feature map. And 4, model training: training neural network parameters byusing a back propagation algorithm. The invention provides a neural network model for pedestrian re-recognition, particularly provides a network structure combining an attention mechanism and attitude estimation based on a double-flow network, and obtains an effect of competitive power in the field of pedestrian re-recognition at present.

Description

technical field [0001] The present invention relates to a deep neural network for person re-identification (Re-ID for short), in particular to a method for combining attitude and attention by using a two-stream network, and combining middle-level features with high-level features Fusion to enhance the expressive power of features. Background technique [0002] Pedestrian re-identification is also called pedestrian re-identification, which literally means re-identifying pedestrians. Re-ID is a technology that uses computer vision technology to determine whether a specific pedestrian exists in an image or video sequence. Widely regarded as a subproblem of image retrieval. Given a monitored pedestrian image, retrieve the pedestrian image across devices. It is designed to make up for the visual limitations of the current fixed camera, and can be combined with pedestrian detection / pedestrian tracking technology, and can be widely used in video surveillance, security and other ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/20G06V40/103G06N3/045G06F18/214
Inventor 朱素果俞俊宫晓伟
Owner HANGZHOU DIANZI UNIV
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