Using Time-of-Flight Sensors for People Counting Applications

Precisely detecting and counting people who are using public transportation is one of the key methods for predicting and planning an efficient use of buses, trams and trains. Providing an effective, well-planned public transportation service is not only important for economic reasons. It also helps to tackle a variety of environmental problems and contributes to a reduction of traffic congestion in urban areas. A couple of such systems had been developed in the past. Those were not sufficiently precise, however. In most cases, these systems rely on data processing generated by one particular type of a 2D image sensor. In this paper we present a robust people counting application, which runs on embedded systems with reasonable requirements as far as computational power is concerned and relies on the processing of 3D data generated by a Time-of-Flight (ToF) sensor. Processing of time-of-flight data requires a couple of preprocessing steps, which is crucial for the subsequent people detection, tracking and counting algorithms. The influence of these preprocessing steps and the effect on the developed detection algorithm are presented. Methods of avoiding misinterpretations by the detection algorithms are discussed. A detailed description of the core algorithms which were developed to process 3D data is provided. An overview will be given on how this method could be further enhanced for the purpose of detecting and differentiating vital and non-vital objects.