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A deep network pedestrian detection method based on shallow feature fusion guidance

A feature fusion, pedestrian detection technology, applied in biometric recognition, neural learning methods, biological neural network models, etc., can solve problems such as missed detection of pedestrian detection algorithms, and achieve good results in solving missed detection, detection accuracy and speed.

Pending Publication Date: 2019-03-29
TAIYUAN UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the deficiencies in the prior art, the present invention provides a deep network pedestrian detection method based on shallow feature fusion guidance, which solves the problem of missed detection by current pedestrian detection algorithms in complex scenes and when the target is too small

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  • A deep network pedestrian detection method based on shallow feature fusion guidance
  • A deep network pedestrian detection method based on shallow feature fusion guidance
  • A deep network pedestrian detection method based on shallow feature fusion guidance

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Embodiment

[0046] (1) Network structure design

[0047] In order to improve the accuracy of pedestrian detection, effectively solve the problem of missed detection in complex scenes and when the target is too small, and enhance the generalization ability of deep learning. Based on Faster R-CNN algorithm detection. Using shallow feature fusion to guide the deep features of the convolutional network, the directional gradient histogram feature, texture feature and convolution feature are fused, with deep learning as the core and shallow features as the guide, realizing the complementary advantages of shallow learning and deep learning. The network structure of the present invention such as figure 1 shown. This network structure design framework includes convolutional layers, pooling layers, and fully connected layers. And an activation function is used in each convolutional layer. After the last convolutional layer, the RPN network is used. The RPN network includes a convolutional layer...

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Abstract

The invention relates to the technical field of computer vision object detection, more specifically, to a deep network pedestrian detection method based on shallow feature fusion guidance. On the basis of CNN detection algorithm, a pedestrian detection method in deep network based on shallow feature fusion is proposed. Through the fusion of directional gradient histogram, improved texture featuresand depth network features, accurate pedestrian features are obtained, which are guided by shallow features with depth learning as the core, and complementary advantages of shallow learning and depthlearning are realized. The results show that the improved method has good performance in detection accuracy and speed. the method effectively solves the technical problem of missing detection in complex scenarios and small targets.

Description

technical field [0001] The invention relates to the technical field of computer vision target detection, and more specifically, to a deep network pedestrian detection method based on shallow feature fusion guidance. Background technique [0002] In recent years, the advantages of the entire digital and networked video surveillance system have become more and more obvious. Its high degree of openness, integration and flexibility provides a broader development space for the development of the entire society's informatization. Pedestrian detection is one of its key technologies. However, due to the complexity of the scene where pedestrians are located, different shapes, and distances, it has become a focus to design a robust and accurate pedestrian detection algorithm. [0003] At present, there are two main categories of methods for pedestrian detection: methods based on manual features and methods based on deep convolutional neural networks (Convolutional Neural Networks, CN...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/10G06N3/045G06F18/253
Inventor 邓红霞马垚杨晓峰李海芳杨雅茹
Owner TAIYUAN UNIV OF TECH
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