Method and system for conducting statistics on elevator visitor flow based on intelligent visual perception

Abstract

The invention provides a method for conducting statistics on elevator visitor flow based on intelligent visual perception. The method comprises the steps of S1. establishing a head-and-shoulder model sample database; S2. conducting feature extraction and model training, wherein samples in S1 are subjected to principle component analysis (PCA) feature extraction, and a support vector machine (SVM) trainer is used for training model generating; S3. detecting targets, wherein matching calculation is conducted on images collected in real time according to human body head-and-shoulder model data obtained in S2, and targets in current images are obtained through detection; S4. tracking targets, wherein targets detected in S3 are tracked; and S5. conducing statistics on the visitor flow, wherein corresponding counters are operated according to incoming and outgoing conditions of targets when targets tracked in S4 go cross a crossing line. The method has the advantages that real-time intelligent analyses are conducted on images collected in real time, so that elevator visitor flow data can be obtained, accurate evidences are provided for establishing of effective and energy-saving elevator dispatching strategies, the problem that prediction models of the elevator visitor flow in traditional methods are complex and difficult to establish is solved, and the imprecise prediction caused by special events are prevented.

Description

technical field [0001] The invention belongs to the field of pattern recognition and computer vision, and in particular relates to a method and system for counting elevator passenger flow based on intelligent visual perception. Background technique [0002] With the increase of high-rise commercial buildings and residential quarters in cities, elevators as an important vertical transportation tool have been more and more widely used. Ensuring reliable, efficient, comfortable and fast operation of elevators is the key to elevator development and technological progress. In order to carry out reasonable and effective scheduling of elevators, it is necessary to have accurate elevator passenger flow data. Therefore, the statistics of passenger flow is particularly important. Because the elevator passenger flow is affected by random factors and is highly nonlinear and unpredictable, it is difficult to find a specific function to predict the elevator passenger flow. [0003] The ...

AI Technical Summary

Problems solved by technology

However, the neural network method has the disadvantages of slow convergence speed and easy to fall into local extremum, which makes it not an ideal method.

[0004] Therefore, in order to solve the problem of difficult establishment of the prediction model of complex elevator passenger flow in the traditional metho

Images