Crowded Pedestrian Detection Method Combining Anchor Free and Anchor Base Algorithm

XIE Minghong; KANG Bin; LI Huafeng; ZHANG Yafei

doi:10.11999/JEIT220444

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1833-1841

XIE Minghong, KANG Bin, LI Huafeng, ZHANG Yafei. Crowded Pedestrian Detection Method Combining Anchor Free and Anchor Base Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1833-1841. doi: 10.11999/JEIT220444

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XIE Minghong, KANG Bin, LI Huafeng, ZHANG Yafei. Crowded Pedestrian Detection Method Combining Anchor Free and Anchor Base Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1833-1841. doi: 10.11999/JEIT220444

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Crowded Pedestrian Detection Method Combining Anchor Free and Anchor Base Algorithm

doi: 10.11999/JEIT220444

XIE Minghong¹,
KANG Bin¹,
LI Huafeng^{1, 2},
ZHANG Yafei^{1, 2
,
,}

1.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China
2.
Key Laboratory of Artificial Intelligence of Yunnan Province, Kunming University of Science and Technology, Kunming 650504, China

Received Date: 2022-04-14
Accepted Date: 2022-09-06
Rev Recd Date: 2022-08-31

Available Online: 2022-09-08

Publish Date: 2023-05-10

Abstract

Abstract

Due to its relatively higher accuracy, the Anchor base algorithm has become a research hotspot for pedestrian detection in crowded scenes. However, the algorithm needs to design manually anchor boxes, which limits its generality. At the same time, a single Non-Maximum Suppression (NMS) screening threshold acting on crowd areas with different densities will lead to a certain degree of missed detection or false detection. To this end, a dual-head detection algorithm combining Anchor free and Anchor base detectors is proposed. Specifically, the Anchor free detector is used to perform rough detection on the image, and the coarse detection results are automatically clustered to generate anchor frames and then fed back to the Region Proposal Network (RPN) module, instead of manually designing the anchor frames in the RPN stage. Meanwhile, the density information of the population in different regions can be obtained through the statistics of the rough detection result information. A pedestrian head-whole body mutual supervision detection framework is designed, and the head detection results and the whole body detection results supervise each other, so as to reduce effectively the suppressed and missed target instances. A novel NMS method is proposed, which can adaptively select appropriate screening thresholds for crowd regions of different densities, thereby minimizing false detections caused by NMS process. The proposed detector is experimentally validated on the CrowdHuman dataset and the CityPersons dataset, achieving comparable performance to current state-of-the-art pedestrian detection methods.
- Pedestrian detection,
- Anchor base,
- Anchor free,
- Non-Maximum Suppression (NMS)

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References(29)

References

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