Bus passenger crowding degree identification system and method

Abstract

The invention relates to a bus passenger crowding degree identification system and method. The system comprises an image collecting module, an image preprocessing module, an image feature extraction module, an image feature training module and an image feature prediction module, wherein the image collecting module is used for collecting monitoring images after a bus leaves a station for one minute, the image preprocessing module is used for segmenting and shearing the collected monitoring images and proportionally zooming the collected monitoring images, the image feature extraction module is used for extracting GLCM (gray level co-occurrence matrix) textural features on the images processed by the image preprocessing module, the image feature training module is used for training according to the textural features extracted by image feature extraction module to obtain an XML (extensive markup language) file, and the image feature prediction module is used for reading the XML file for crowding degree training and prediction for completing the prediction for the passenger crowding degree in the monitoring images. The system and the method have the advantages that the monitoring images of the existing vehicle-mounted monitoring equipment can be sufficiently reused for recognition, the detection results of the images monitored by a plurality of cameras in the bus are synthesized, an accurate recognition on three stages of crowding degrees can be realized, and higher economic performance, reliability and high efficiency are realized.

Description

technical field [0001] The invention relates to a system and method for identifying the degree of crowding of bus passengers. Background technique [0002] With the continuous increase of people's social activities, incidents of casualties caused by excessive crowd density are not uncommon. In daily life, it is very necessary to count the crowd density of places such as subways, stations, and supermarkets that people often come and go. Therefore, crowd density analysis has broad application prospects and research value. The traditional crowd density analysis is manually monitored through the closed-circuit television of the monitoring scene, which is time-consuming, laborious and lacks objectivity. With the development and wide application of computer and image processing technology, intelligent crowd density monitoring system came into being. [0003] There are two main methods for crowd density estimation: the density estimation method based on pixel statistics is relati...

AI Technical Summary

Problems solved by technology

[0003] There are two main methods for crowd density estimation: the density estimation method based on pixel statistics is relatively simple, but the error is large when the crowd density is high and the crowd occlusion is serious; the method of using texture analysis can make full use of the texture information of the image, but the algorithm is complex high degree

The existing crowd density analysis method only calculates the crowd density of the whole image in general and ignores the local area, and there is obvious overlap between people in the high-density crowd, so there are various advantages in the selection of descriptors for feature extraction. This limitation and one-sidedness lead to a high misjudgment rate in crowd density analysis

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