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Method for detecting pedestrians in crowding scene

A crowded scene, pedestrian detection technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve problems such as complex spatial relationships, people-to-people occlusion, and failure to achieve results, so as to improve detection accuracy and adapt to a wide range Effect

Active Publication Date: 2012-12-05
JIANGSU CAS JUNSHINE TECH
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AI Technical Summary

Problems solved by technology

The above two traditional methods perform well in ordinary scenes, but cannot achieve satisfactory results in crowded scenes due to severe occlusion and complex spatial relationships between people.

Method used

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  • Method for detecting pedestrians in crowding scene
  • Method for detecting pedestrians in crowding scene
  • Method for detecting pedestrians in crowding scene

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Embodiment Construction

[0018] The present invention will be further described below in conjunction with specific drawings and embodiments.

[0019] Such as figure 1 Shown: the present invention is used for a plurality of pedestrian detection methods under the crowded scene and comprises the following steps:

[0020] a, input the training collection that contains a plurality of training sample images, utilize K-means clustering to divide the pedestrian in the training sample image into several subcategories, each subcategory corresponds to a kind of pedestrian change;

[0021] b. Use the block model to represent the appearance of pedestrians, use the deformable component model to automatically block pedestrians, each block has a corresponding score, and set the corresponding weight vector for each sub-category to establish pedestrian appearance characteristics Model,

[0022] c. Establish the pedestrian spatial relationship model on the training sample set by using the quadratic kernel function;

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Abstract

The invention relates to a method for detecting a plurality of pedestrians in a crowding scene. The method comprises the following steps: a, inputting a training set containing a plurality training sample images, and dividing pedestrians in the training sample images into a plurality of subclasses, wherein each subclass is corresponding to one pedestrian change; b, establishing a training set pedestrian representation feature model; c, establishing a spatial relational model of pedestrians on the training set; d, establishing a probabilistic model to describe the pedestrians in the crowding scene, and converting the probabilistic model into an energy objective function; e, looking for the optimal parameter of the energy objective function in a parameter learning method based on a latent rank SVM (Support Vector Machine) so as to obtain a determined energy objective function; and f, inputting crowding scene pedestrian images to be detected, and detecting by a model deduction method based on expansion move and the determined energy objective function to obtain the result of the crowding scene pedestrian images to be detected. According to the method disclosed by the invention, the detection precision is improved, and the application range of the method is wide.

Description

technical field [0001] The invention relates to a detection method, in particular to a detection method for multiple pedestrians in a crowded scene, and belongs to the technical field of image processing and pattern recognition. Background technique [0002] Pedestrian detection in real scenes plays an important role in many computer vision applications, such as video surveillance and assisted driving systems, and robust pedestrian detection is also one of the prerequisites for improving other intelligent video analysis techniques. [0003] Traditional pedestrian detection methods can be roughly divided into two categories: the first category is based on template matching methods, first train a classifier, and then use different scales of search windows in the image to be detected to perform sliding frame detection on the entire image, and judge Whether there is a pedestrian in the sliding frame; the second category adopts the Hough voting method, uses the implicit shape mod...

Claims

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Application Information

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IPC IPC(8): G06K9/66
Inventor 李子青闫俊杰雷震张旭聪易东
Owner JIANGSU CAS JUNSHINE TECH
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