Research Advanced in Pedestrian Detection Based on Deep Learning

Abstract

In the field of computer vision, pedestrian detection is a significant research area that is frequently applied in areas like intelligent driving and video surveillance. The reliability and accuracy of subsequent tasks like pedestrian tracking and behavior analysis are directly impacted by the accuracy and effectiveness of pedestrian detection. However, there are still a number of difficulties with pedestrian detection tasks because of the variety of factors, including the shape, scale, and posture of pedestrians, as well as background interference and changes in lighting. Based on research on pertinent domestic and international literature, this article examines the key techniques for identifying pedestrians. Introduce frequently used datasets for pedestrian detection first. The model can then be split into two categories based on its training process: one-stage detection algorithms and two-stage detection algorithms. For each stage type, this study offers a thorough introduction to representative algorithms. A summary of its current state of development and future prospects was then given.