Abstract
Making events recognition more reliable under complex environment is one of the most important challenges for the intelligent recognition system to the ticket gate in the urban rapid rail transit. The motion objects passing through the ticket gate could be described as a series of moving sequences got by sensors that located in the walkway side of the ticket gate. This paper presents a robust method to detect some classes of events of ticket gate in the urban rapid rail transit. Diffused reflectance infrared sensors are used to collect signals. In this paper, the motion objects are here referred to passenger(s) or (and) luggage(s), for which are of frequent occurrences in the ticket gate of the urban railway traffic. Specifically, this paper makes two main contributions: 1) The proposed recognition method could be used to identify several events, including the event of one person passing through the ticket gate, the event of two consecutive passengers passing through the ticket gate without a big gap between them, and the event of a passenger walking through the ticket gate pulling a suitcase; 2) The moving time sequence matrix is transformed into a one-dimensional vector as the feature descriptor. Deep learning (DL), back propagation neural network (BP), and support vector machine (SVM) are applied to recognize the events respectively. BP has been proved to have a higher recognition rate compared to other methods. In order to implement the three algorithms, a data set is built which includes 150 samples of all kinds of events from the practical tests. Experiments show the effectiveness of the proposed methods.
Highlights
The automatic fare collection (AFC) system has received a great deal of attention from the industrial and scientific communities over the past several decades owing to its wide range of applications in preventing stealing a ride, access control, law enforcement, etc
Note that the method gives perfect recognition results for database build in this paper: deep learning (DL) gives 89.2%, Back Propagation Neural Network (BP) gives 92.5%, and support vector machine (SVM) gives 90.0%
BP shows a higher recognition rates both in events 3 (100%) and all events referred in this paper (92%), with recognition rates of DL and SVM 90%, 89% and 90%, 90%
Summary
The automatic fare collection (AFC) system has received a great deal of attention from the industrial and scientific communities over the past several decades owing to its wide range of applications in preventing stealing a ride, access control, law enforcement, etc. It is a significant value study in the intelligent recognition system. The method combines event recognition technology, human gait recognition technology and human body contour recognition technology This method has somewhat difficulty in dealing with the transit without a big gap between two walking passengers. In order to improve the recognition rate, a novel method is proposed
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