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Bus passenger flow statistics algorithm based on deep learning
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DOI：10.1007/s11042-020-09487-0.png)
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Abstract
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Bus passenger flow statistics can be used to improve passenger travelling experience and reduce trip delay, this is very important for intelligent transportation. In this paper, a bus passenger flow statistics algorithm based on SSD (Single Shot MultiBox Detector) and Kalman filter is proposed to obtain passenger flow statistics from surveillance cameras on the buses. The method modifies the SSD model to a two-class model and trains the two-class SSD model using the bus dataset first, then the model is used to detect the position of the passengers in each frame and are tracked with the Kalman filter. Finally, according to the passenger trajectory, the traffic statistics of passenger getting on and off will be generated. The results of some conducted experiments show that the proposed bus passenger flow statistics algorithm is more accurate and robust than traditional methods.
Keywords:
Computer vision
Deep learning
Bus passenger flow statistics
SSD algorithm
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