Passenger and pedestrian recognition based on neural networks and deep learning in stations

Abstract

Pedestrian detection technology has high application value in various fields, and deep learning has become a key development direction in computer vision. Human object detection has also shifted from traditional detection algorithms to deep learning. Due to the influence of complex light and obstacles in the station, as well as the occlusions and size changes of passengers, the algorithm must be optimized for these complex scenes. This paper takes pedestrian detection technology as the goal, compares the methods based on human body parts recognition from the concepts and classification of artificial neural networks and deep learning, and profoundly discusses the convolutional neural network based on deep learning. Finally, pedestrian detection algorithms' problems and future trends are compared and discussed.