A precise counting of passengers entering and exiting means of transport has a positive effect on public transport surveillance, passenger flow prediction, transport planning, transport vehicle load monitoring, station control and management, and cost optimization. One of the basic measures, which must be provided by the system, is the number of transported passengers. In some areas of public transport, passenger flow monitoring systems are employed to automate this task. In passenger transport, person flow monitoring has an indispensable importance.
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To allow verification of theoretical findings, we construct an engineering prototype of the system. For an appropriate setting, it defeats the ConvNets in terms of time complexity while keeping excellent classification performance. We demonstrate that, compared to ConvNets, the HOG-based passenger recognition is more suitable for practical applications. For this purpose, we form and make publicly available a large-scale, class-balanced dataset of labelled RGB images. Specifically, we examine classification performance and time complexity of the approaches for various topologies and settings, respectively. We study both approaches in terms of practical applications, where real-time processing of data is one of the basic assumptions. This approach is a representative of classical methods. The second approach is the utilization of histograms of oriented gradients (HOG) features in combination with a support vector machine classifier. The first approach is the utilization of an appropriate convolutional neural network (ConvNet), which is currently the prevailing approach in computer vision. We present two opposite approaches which can be used for the passenger recognition in means of transport with and without entrance steps, or with split level flooring. As the precision of the counting is relevantly influenced by the precision of passenger recognition, we focus on the development of an appropriate recognition method.
To guarantee the anonymity of passengers, we suggest orthogonal scanning of a scene. As the overall price is mainly given by prices of the used sensor and processing unit, we propose the utilization of a visible spectrum camera and data processing algorithms of low time complexity to ensure a low price of the final product. To allow mass utilization of passenger flow monitoring systems, their cost must be low. Exact numbers of passengers are important in areas such as public transport surveillance, passenger flow prediction, transport planning, and transport vehicle load monitoring. Counting of passengers entering and exiting means of transport is one of the basic functionalities of passenger flow monitoring systems.
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