Single-Pedestrian Detection Aided by Multi-pedestrian Detection
Abstract
In this paper, we address the challenging problem of detecting pedestrians who appear in groups and have interaction. A new approach is proposed for single-pedestrian detection aided by multi-pedestrian detection. A mixture model of multi-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby multiple pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single-and multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection. It can integrate with any single-pedestrian detector without significantly increasing the computation load. 15 state-of-the-art single-pedestrian detection approaches are investigated on three widely used public datasets: Caltech, TUD-Brussels and ETH. Experimental results show that our framework significantly improves all these approaches. The average improvement is 9% on the Caltech-Test dataset, 11% on the TUD-Brussels dataset and 17% on the ETH dataset in terms of average miss rate. The lowest average miss rate is reduced from 48% to 43% on the Caltech-Test dataset, from 55% to 50% on the TUD-Brussels dataset and from 51% to 41% on the ETH dataset.
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Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_39 · Cited by 488 · see citing works
Filtered channel features for pedestrian detection
Zhang, S., Benenson, R., & Schiele, B. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298784 · Cited by 395 · see citing works
EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes
Braun, M., Krebs, S., Flohr, F., & Gavrila, D. M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2897684 · Cited by 307 · see citing works
Improved non-maximum suppression for object detection using harmony search algorithm
Song, Y., Pan, Q.-K., Gao, L., & Zhang, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.05.005 · Cited by 41 · see citing works
- Authors
- Ouyang, W., & Wang, X.
- Affiliations
- Shenzhen Institutes of Advanced Technology (CN)
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 3198-3205
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2152945944
- Cited by
- 144
- References
- 98
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 9 · 2020: 7 · 2019: 13 · 2018: 19 · 2017: 15 · 2016: 17 · 2015: 27 · 2014: 17 · 2013: 5
- FWCI
- 14.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- pedestrian detection, mixture models, probabilistic framework, visual cues
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