Abstract

Pedestrian detection is widely applied in surveillance, autonomous robotic navigation, and automotive safety. However, there are many occlusion problems in real life. This paper summarizes the research progress of pedestrian detection technology with occlusion. First, according to different occlusion, it can be divided into two categories: inter-class occlusion and intra-class occlusion. Second, it summarizes the traditional method and deep learning method to deal with occlusion. Furthermore, the main ideas and core problems of each method model are analyzed and discussed. Finally, the paper gives an outlook on the problems to be solved in the future development of pedestrian detection technology with occlusion.

Highlights

  • Pedestrian detection technology is a computer for the given video and image, to determine it is pedestrians, and mark the location of pedestrians

  • The rapid development of artificial intelligence technology makes pedestrian detection set off a new upsurge in the field of computer vision

  • Pedestrian detection algorithms based on traditional methods and deep learning are introduced

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Summary

Motivation

Pedestrian detection technology is a computer for the given video and image, to determine it is pedestrians, and mark the location of pedestrians. Pedestrian detection provides technical support and foundation for gait analysis, pedestrian identification, pedestrian analysis These technologies are widely applied in video surveillance [1,2,3,4], self-driving cars [5,6,7,8], autonomous robots [9, 10] and many other fields. Vehicle-assisted driving systems, intelligent video surveillance, robotics, human— computer interaction systems, and security work all benefit from occluded pedestrian detection. Pedestrian detection under occlusion is one of the important foundations of the above directions. The detection results are helpful to risk management of driving behavior and improve driving safety This has been playing an important role in ensuring the traffic safety of modern urban. The deep learning algorithms have made it great progress, it has Complex & Intelligent Systems (2021) 7:577–587

Method
Evaluation of multiple databases
Training data problem
Long-term occlusion or heavy occlusion problem
Conclusion
Compliance with ethical standards

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