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Generalized density crowd counting method based on multi-scale depth learning

A technology of deep learning and crowd counting, applied in the field of computer vision and intelligent transportation, can solve problems such as poor effect

Active Publication Date: 2019-03-29
FUDAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These methods have good results in a single fixed sparse scene, but in different scenes, different density levels of crowd density (for example, the crowd density changes from extremely sparse to extremely dense) the effect is poor

Method used

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  • Generalized density crowd counting method based on multi-scale depth learning
  • Generalized density crowd counting method based on multi-scale depth learning
  • Generalized density crowd counting method based on multi-scale depth learning

Examples

Experimental program
Comparison scheme
Effect test

experiment example 1

[0071] Experimental example 1: The performance of the algorithm on ShanghaiTech data

[0072] Table 1: Performance comparison of the algorithm in the ShanghaiTech dataset and other methods

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experiment example 2

[0074] Experimental Example 2: The performance of the algorithm on the UCF_CC_50 dataset

[0075] Table 2: Performance comparison of the algorithm in the UCF_CC_50 dataset and other methods

[0076]

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experiment example 3

[0078] Experimental example 3: The performance of the algorithm on the UCSD dataset

[0079] Table 3: Performance comparison of the algorithm on the UCSD dataset and other methods

[0080] Method

[0081] .

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PUM

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Abstract

The invention belongs to the technical field of computer vision and intelligent transportation, in particular to a generalized density crowd counting method based on multi-scale depth learning, whichis called PaDNet. The invention uses a plurality of sub-networks to learn specific crowd scale characteristics. Firstly, the data set is clustered, and the data set is divided into a plurality of density grades: low density data set, medium density data set and high density data set. Using the data of a specific density level to pre-train a specific sub-network, and then a scale enhancement network is used to enhance the scale characteristics; Finally, all the scale features are merged into the final density map through a converged network to count them. The invention can enable the specific sub-network to learn the accurate scale characteristics, so that different networks can identify the crowd characteristics of different density grades and more accurately count the crowd.

Description

technical field [0001] The invention belongs to the technical field of computer vision and intelligent transportation, and in particular relates to a crowd counting method based on multi-scale deep learning. Background technique [0002] Crowd counting is one of the difficulties and hotspots in the field of computer vision and machine learning. When a crowd image is given, it is required to output the number of pedestrians based on computer vision or machine learning algorithms. At present, there are many previous works in this field, and the main methods can be divided into two categories: detection-based methods and feature regression-based methods. Here are some references for both types of methods: [0003] [1] Dalal, N., and Triggs, B. 2005. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1,886–893. [0004] [2] Ren, S.; He, K.; Girshick, R.; and Sun, J.2017. Faster RCNN...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V10/50G06V10/462G06N3/045G06F18/23G06F18/253
Inventor 田宇坤张军平
Owner FUDAN UNIV
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