Occlusion Handling and Multi-scale Pedestrian Detection Based on Deep Learning

Occlusion Handling and Multi-scale Pedestrian Detection Based on Deep Learning: A Review

Li, Fang (Beijing Institute of Technology)
Li, Xueyuan (Beijing Institute of Technology)
Liu, Qi (Beijing Institute of Technology)
Li, Z. (TU Delft Transport and Planning; Beijing Institute of Technology)

2022

Pedestrian detection is an important branch of computer vision, and it has important applications in the fields of autonomous driving, artificial intelligence and video surveillance.With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and achieves better performance. However, the performance of state-of-the-art methods is far behind the expectation, especially when occlusion and scale variance exist. Therefore, a lot of works focused on occlusion and scale variance have been proposed in the past few years. The purpose of this article is to make a detailed review of recent progress in pedestrian detection. Firstly, brief progress of pedestrian detection in the past two decades is summarized. Secondly, recent deep learning methods focusing on occlusion and scale variance are analyzed. Moreover, the popular datasets and evaluation methods for pedestrian detection are introduced. Finally, the development trend of pedestrian detection is prospected.

Deep learning
Detectors
Feature extraction
Lighting
Object detection
occlusion handling
pedestrian detection
Proposals
Real-time systems
scale variance

http://resolver.tudelft.nl/uuid:8c595179-9f10-4026-be05-2bdaf782021c

https://doi.org/10.1109/ACCESS.2022.3150988

2169-3536

IEEE Access, 10, 19937-19957

Institutional Repository

journal article

© 2022 Fang Li, Xueyuan Li, Qi Liu, Z. Li