Real-Time Statistics Method of Escalator Passenger Flow for Embedded Devices

For the difficulty in balancing the accuracy and speed of the traditional statistical calculation method, this study proposed a real-time statistics method of escalator passenger flow for embedded devices. Firstly, a distortion-free scaling method was proposed to maintain the consistency of information between the test and the training sample to avoid affecting the performance of the detection model. Sencondly, the YOLOv4-tiny detection model was optimized by a dimensionality reduction module and group convolution, and a YOLOv4-tiny-fast network was proposed, which significantly reduces the number of parameters and improves the inference speed while ensuring no loss of passenger detection accuracy. Finally, a matching algorithm combining custom optimization matrix and occlusion processing was proposed to solve the passenger tracking problem with less computational effort. The experiment was conducted with video of escalator entrances and exits in a real environment. The results show that the proposed algorithm achieves an average accuracy of 96.66% in passenger flow statistics on the embedded device platform, and the average detection speed reaches 25 f/s which is superior to existing algorithms.