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.