Method and system for predicting people crowdedness

Abstract

The invention discloses a method for predicting people crowdedness and the method comprises: first, obtaining the image frame sequence of a to-be-measured area; inputting the image frame sequence into a pedestrian detection module; through the pedestrian detection module, obtaining the characteristic vectors of the human face images; through the characteristic vectors of the human face images of two adjacent frames, obtaining and outputting the number of the traveling people; based on the number of the traveling people, calculating the people density; and inputting the people density into a people crowdedness predicting module to calculate and output the people crowdedness predicting vector signal. According to the method and the system of the invention, a support vector machine is utilized to train the predicting model, which increases the computation efficiency of the system and the precision of the predicting result so that the method and the system become more adaptable to the data and the environment. In addition, as the method and the system are equipped with a people crowdedness training module and a historical database, the system is empowered with the ability for self-adaptive learning, and therefore, the manual involvement is reduced, the human resource and use difficulty can be saved, making the method and system adaptable to complex and diverse environments. Along with the increase in the learning time and the enlargement of the historical data, the people crowdedness predicting model can achieve more precise predicting results, and the cost in doing so is also reduced.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and system for predicting crowd flow congestion. Background technique [0002] In public scenes and traffic control, it is often necessary to determine the pedestrian flow in the space area to be detected, which is generally convenient for real-time control of crosswalk signal lights, or to ensure the safety of public places. In recent years, a large number of surveillance cameras have been added in cities across the country. After obtaining a large amount of video data through the camera, the traffic statistics data in the video data can be accurately extracted; however, the workload of manual analysis of video data is very large and error-prone, so intelligent video surveillance has become the focus of domestic and foreign researchers. Among them, pedestrian flow counting has become a very important research field in intelligent video surveillance because of its wide...

AI Technical Summary

Problems solved by technology

After obtaining a large amount of video data through the camera, the traffic statistics data in the video data can be accurately extracted; however, the workload of manual analysis of video data is very large and error-prone, so intelligent video surveillance has become the focus of domestic and foreign researchers.

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