Pedestrian volume statistical method based on plurality of Gaussian counting models

A statistical method and technology of people flow, applied in the field of people flow statistics, can solve the problem of inaccurate statistics of high-density floating people, and achieve the effect of reducing computational complexity

Active Publication Date: 2012-07-04
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0008] The present invention aims at the above-mentioned problems existing in the existing computer vision-based people flow statistics technology, and proposes a real-time people flow statist

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  • Pedestrian volume statistical method based on plurality of Gaussian counting models
  • Pedestrian volume statistical method based on plurality of Gaussian counting models
  • Pedestrian volume statistical method based on plurality of Gaussian counting models

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Embodiment Construction

[0025] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0026] A kind of people flow statistics based on multi-Gauss counting model proposed by the present invention is used in real-time monitoring system. The foreground moving target is obtained through motion detection, and the number of people in the current image is analyzed according to the multi-Gaussian counting model to realize people flow statistics.

[0027] figure 1 It is a flow chart of people flow statistics based on the multi-Gaussian counting model in the embodiment of the present invention. Such as figure 1 As shown, in the embodiment of the present invention, a multi-Gaussian counting model needs to be established before the people flow statistics are performed.

[0028] Segment the region of interest in the scene into a series of detection sub-regions. Smoothing and filtering are performed on the segmented video images.

[0...

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Abstract

The invention relates to intelligent video surveillance and image processing and analysis, discloses a pedestrian volume statistical method, and comprises establishing a plurality of Gaussian counting models by utilizing training video sequence image samples with people number marks and performing real-time pedestrian volume statistics on videos with unknown people numbers based on the plurality of Gaussian counting models. The pedestrian volume statistical method particularly comprises the steps of firstly extracting a prospect moving target according to moving target detection, extracting eigenvectors according to moving target area and characteristics including lengths and widths of an external rectangular frame, then establishing the plurality of Gaussian counting models based on an eigenvector set, and finally analyzing numbers of pedestrians contained in an unknown moving target area based on the plurality of Gaussian counting models to achieve pedestrian volume statistics. By establishing the plurality of Gaussian counting models, the pedestrian volume statistical method avoids difficulties caused by identification and tracking of singe pedestrian, can perform statistics of the numbers of the pedestrians contained in moving target areas in different detection areas well, improves statistical accuracy of the numbers of the pedestrians, and then improves accuracy of the pedestrian volume statistics.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and system for counting people flow. Background technique [0002] In the existing video surveillance, most of them simply realize video transmission, and then rely on human eye observation to realize scene monitoring and counting. This human monitoring method has a lot of shortcomings, such as being boring, and the monitoring personnel are prone to fatigue and lead to work mistakes. In addition, with the increase of labor costs, relying on the method of manpower monitoring and counting will no longer be suitable. [0003] At present, the methods used in the computer vision-based people flow statistics system can be divided into three categories: one is the method based on pedestrian detection and tracking; the other is the method based on feature point trajectory clustering; the third is the method based on low-level feature regression. [0004] The co...

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Application Information

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IPC IPC(8): G06K9/62G06T7/20
Inventor 高陈强余迪虎李璐星李强查力
Owner CHONGQING UNIV OF POSTS & TELECOMM
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