Pedestrian recognition method based on combination of depth learning and property learning

A pedestrian recognition and deep learning technology, applied in the field of pattern recognition, can solve problems such as nonconformity, affect recognition ability, lack of semantic expression ability, etc., and achieve the effect of good recognition rate, good semantic expression ability, and cost reduction.

Active Publication Date: 2015-10-21
南京昭视智能科技有限公司
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Problems solved by technology

The disadvantage is that these low-level feature data do not have good semantic expression ability. In addition, when using the low-level feature data, it is usually

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  • Pedestrian recognition method based on combination of depth learning and property learning
  • Pedestrian recognition method based on combination of depth learning and property learning
  • Pedestrian recognition method based on combination of depth learning and property learning

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Embodiment Construction

[0033] The invention is based on a pedestrian recognition method combining deep learning and attribute learning, and is divided into four parts: deep learning, attribute learning, attribute category mapping relationship learning and testing. It combines deep learning with attribute learning to extract the preferred features of images and represent them with better semantics. Among them, deep learning is divided into two stages: building a deep learning model and model training. In the stage of building a deep learning model, construct a multi-layer convolutional neural network model, initialize the model and set the relevant parameters of the model; The method tunes the parameters of the convolutional neural network. In the attribute learning part, an attribute classifier is set for each attribute. The preferred features trained from the convolutional neural network model are input to each classifier, and the classifiers learn the attributes through the attribute labels of t...

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Abstract

The invention discloses a pedestrian recognition method based on combination of depth learning and property learning. According to the invention, a convolution neural network containing five implicit strata is constructed. Network training is performed by an anti-convolution method and a concept of property learning is combined. Preferred features obtained from the convolution neural network are input to property classifiers, so that the posterior probability of the property of a sample is obtained. Then by combining with a property class mapping relation, the posterior probability of the class is obtained, so that the class of the sample can be judged. The method is good in detection recognition performance and intrinsic features of an image can be extracted. Besides, since the property has better semantic expression performance than low-stratum features and due to the insensitivity to light and view angles, the algorithm has a good recognition effect.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a pedestrian recognition method based on the combination of deep learning and attribute learning. Background technique [0002] Pedestrian recognition has broad application prospects in video control, robotics, intelligent transportation, multimedia retrieval and other fields, and it is also a popular research object in the field of computer vision in recent years. However, because pedestrian recognition involves the calculation and analysis of a large amount of data, plus the interference of environmental factors such as light and viewing angle, traditional recognition algorithms cannot extract the preferred features of the image and express them through better semantics, resulting in a limited recognition rate. [0003] A traditional recognition algorithm for pedestrian recognition is artificial neural network. It abstracts the human brain neuron network from the p...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06K9/66
CPCG06V40/10G06V30/194G06F18/2411
Inventor 成科扬张纯许方洁王卫东汪树胜羊立
Owner 南京昭视智能科技有限公司
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