The statistics of traditional and typical bus passengers have some shortcomings in accuracy and speed, and the effect of extracting target features is poor. This study proposes a bus counting system based on deep convolutional neural network to solve the crowd counting problem. The first thing to make a dataset is that all the datasets used for training are hand-labeled. And the bus camera angle is wider than the previous literature. This study first compares the effects of various deep convolutional neural network models on the whole body detection of passengers. Considering the detection rate and accuracy, the single-detector deep convolutional neural network model is used to detect passengers' heads. The simple online and real-time target tracking algorithm implements multi-target tracking of human heads, and the cross-region crowd counting method is used to count the number of passenger getting off the bus. The system accuracy rate reaches 78.38% and the operating rate is approximately 19.79 frames per second. the passenger count is achieved.