Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases
Open accessAbstract
Pedestrian detection is a particular case of object detection that helps to reduce accidents in advanced driver-assistance systems and autonomous vehicles. It is not an easy task because of the variability of the objects and the time constraints. A performance comparison of object detection methods, including both GPU and non-GPU implementations over a variety of on-road specific databases, is provided. Computer vision multi-class object detection can be integrated on sensor fusion modules where recall is preferred over precision. For this reason, ad hoc training with a single class for pedestrians has been performed and we achieved a significant increase in recall. Experiments have been carried out on several architectures and a special effort has been devoted to achieve a feasible computational time for a real-time system. Finally, an analysis of the input image size allows to fine-tune the model and get better results with practical costs.
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Single-stage detectors find applications in various domains, including autonomous vehicles, surveillance, robotics, and object recognition in images and videos, and their speed and simplicity make them well-suited for real-time detection scenarios.
“Single-stage detectors find applications in various domains, including autonomous vehicles, surveillance, robotics, and object recognition in images and videos. Their speed and simplicity make them particularly well-suited for scenarios where real-time detection is crucial. These applications are supported by numerous studies that highlight the efficiency and effectiveness of single-stage detec”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [27]
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nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
Improving single-stage object detectors for nighttime pedestrian detection
Devi, S., Thopalli, K., Malarvezhi, P., & Thiagarajan, J. J. (2022) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001422500343 · Cited by 15 · see citing works
- Authors
- Ortiz Castelló, V., del Tejo Catalá, O., Salvador Igual, I., & Perez-Cortes, J.-C.
- Affiliations
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València, Valencia, Spain
- Venue
- International Journal of Advanced Robotic Systems
- Publisher
- SAGE Publications
- Published
- 2020-09-01
- Volume
- 17
- Issue
- 5
- ISSN
- 1729-8806 (ISSN-L)
- 1729-8814
- OpenAlex
- W3088843444
- Cited by
- 7
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 1
- FWCI
- 0.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Advanced Robotic Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Funders
- European Commission (783190)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- pedestrian detection, real-time object detection, one-stage object detection, driver assistance systems, autonomous vehicles, sensor fusion, multi-class object detection, memory retrieval, computational cost
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