Pedestrian detection in automotive safety: Understanding state-of-the-art
Open accessAbstract
Pedestrian detection is one of the important computer vision problems in automotive safety and driver assistance domain. It is a major component of the advanced driver assistance system (ADAS) which help the driver to drive safely. Recent literature shows a number of research activities addressing object detection/tracking in general and pedestrian detection in particular. The solutions proposed by different researchers vary in detection methods, detection scenario, feature descriptors, classification schemes, detection performance, as well as computational complexity. However, the average detection accuracy is not much promising even after many years of research. The fail-safe and real-time human detection from real life road scenes, even in standard resolution, is far from reality. Safety critical systems in the automotive industry have to follow well established stringent safety standards like ISO26262. Since the pedestrian detection system deals with human safety, it also has to follow these standards before integrating to the vehicle electronics. This paper is a study of different techniques used in pedestrian detection specific to the automotive application, along with a description of generic pedestrian detection solution architecture.
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The composition and quality of datasets for training and testing directly impact the efficacy of a DL-and feature-selection-driven pedestrian detection system.
“One of the paper's key findings is that the composition and quality of the datasets for training and testing that are used in the detection phase have a direct impact on the system's efficacy.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [144]
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Tahir, N. U. A., Zhang, Z., Asim, M., Chen, J., & ELAffendi, M. (2024) · Algorithms · doi:10.3390/a17030103 · Cited by 145 · see citing works
- Authors
- Ragesh, N. K., & Rajesh, R.
- Affiliations
- Tata Elxsi (India) (IN)
- Central University of Kerala (IN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 47864-47890
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2940510674
- Cited by
- 57
- References
- 9
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 11 · 2023: 7 · 2022: 8 · 2021: 11 · 2020: 8 · 2019: 1
- FWCI
- 3.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- 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
- Keywords
- pedestrian detection, vehicle safety, driver assistance systems, object detection, real-time human detection, traffic scenes, ISO 26262, safety-critical systems, detection accuracy, feature descriptors, classification systems, computational complexity
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