부산대학교 도서관

Bus-crowdedness information is essential not only for a bus company to arrange schedules but also for passengers to plan their trip. This paper presents a very shallow Single camera based Convolutional Neural Networks (SCNN) and Multi-cameras based Convolutional Neural Networks (MCNN) to estimate the crowdedness of a bus. The images from front and back cameras are combined as input for the SCNN and MCNN models. To make the model robust enough, some strategies to generate samples are proposed, such as random cropped and random white balance. In order to minimize model, we try to reduce convolutional layers, reduce the size of convolution kennel and enlarge the stride of the max-pooling layer, instead of dropping the model. As such, the forward calculations of SCNN and MCNN are essentially reduced. The models are so lightweight that can run on an embedded system carried on the bus. Trace-based simulation demonstrate the viability of our design choices. Using the model to estimate the crowdedness level of the bus get a very high accuracy of 99.1%. It can be done in real time with high robustness.