Pedestrian retrieval enhancement method based on pedestrian detection, attribute learning and pedestrian recognition

A technology of pedestrian detection and attribute learning, which is applied in character and pattern recognition, biometric recognition, instruments, etc., can solve problems such as failure to achieve good results, semantic deviation affecting the accuracy of pedestrian retrieval, and different information emphases

Inactive Publication Date: 2019-10-15
HEFEI UNIV OF TECH
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  • Abstract
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AI Technical Summary

Problems solved by technology

On the downside, the semantic bias between the pose dataset and the pedestrian retrieval dataset can affect the accuracy of pedestrian retrieval.
[0003] On the other hand, due to the different information carried by different features, the use of a single feature cannot achieve better results.

Method used

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  • Pedestrian retrieval enhancement method based on pedestrian detection, attribute learning and pedestrian recognition
  • Pedestrian retrieval enhancement method based on pedestrian detection, attribute learning and pedestrian recognition
  • Pedestrian retrieval enhancement method based on pedestrian detection, attribute learning and pedestrian recognition

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Embodiment Construction

[0029] like figure 1 As shown, a pedestrian retrieval enhancement method based on pedestrian detection, attribute learning and pedestrian recognition includes the following steps:

[0030] S1. Pedestrian detection

[0031] The first step in pedestrian retrieval is to detect the area where pedestrians are located in a given image. Currently, Faster R-CNN has achieved better results in this work. Faster-RCNN is based on deep learning, has unity and high precision, and is currently a very popular target detection method; when detecting pedestrians from pedestrian images, Faster-RCNN is used as a detector. This detector combines RPN and FastR-CNN into one network by sharing convolutional features; as a fully convolutional network, RPN can simultaneously predict object boundaries and scores for each location; when detecting pedestrians, first generate high-quality The region proposals of , and then use the FastR-CNN network for detection; according to these definitions, FasterR-C...

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Abstract

The invention discloses a pedestrian retrieval enhancement method based on pedestrian detection, attribute learning and pedestrian recognition. The method comprises the steps of pedestrian detection,pedestrian re-identification, pedestrian attribute prediction, pedestrian detection loss function, attribute classification loss function and identity classification loss function are used, attributeand identity labels are used to identify the position of a pedestrian in an image through a training framework, and a final loss function is obtained. According to the method, a multi-task deep learning framework is developed to solve the pedestrian retrieval problem, pedestrian detection, pedestrian re-identification and pedestrian attribute prediction are comprehensively considered in the framework in a single convolutional neural network, and the retrieval precision is improved.

Description

technical field [0001] The invention relates to a pedestrian retrieval enhancement method based on pedestrian detection, attribute learning and pedestrian identification. Background technique [0002] Pedestrian retrieval has received increasing attention due to its important role in video surveillance, which aims to retrieve persons of interest in multiple non-overlapping camera views. Given a picture, the task is to rank all pedestrian images candidates by calculating the similarity / distance between the picture and candidate images, and return the most relevant image as the retrieval result. It mainly consists of two parts: feature extraction and metric learning. The first part focuses on designing more robust features. The second part is to learn an appropriate distance / similarity function, using the features extracted from the image to better describe the similarity relationship between similar / different samples. In the early work, most of the work only used one featu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/10G06N3/045G06F18/2155G06F18/241
Inventor 刘学亮杜海骏汪萌洪日昌徐超峰
Owner HEFEI UNIV OF TECH
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