Improved Candidate Generation for Pedestrian Detection using Background Modeling in Connected Vehicles
| Autor: | Ghaith Al-Refai, Osamah Rawashdeh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science business.industry Pedestrian detection Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) Image registration Image processing 0102 computer and information sciences 02 engineering and technology 01 natural sciences Image (mathematics) 010201 computation theory & mathematics Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 11 |
| ISSN: | 2156-5570 2158-107X |
| Popis: | Pedestrian detection is widely used in today’s ve-hicle safety applications to avoid vehicle-pedestrian accidents. The current technology of pedestrian detection utilizes onboard sensors such as cameras, radars, and Lidars to detect pedestrians, then information is used in a safety feature like Automatic Emer-gency Braking (AEB). This paper proposes pedestrian detection system using vehicle connectivity, image processing and computer vision algorithms. In the proposed model, vehicles collect image frames using on-vehicle cameras, then frames are transferred to the Infrastructure database using Vehicle to Infrastructure communication (V2I). Image processing and machine learning algorithms are used to process the infrastructure images for pedestrian detection. Background modeling is used to extract the foreground regions in an image to identify regions of interest for candidate generation. This paper explains the algorithms of the infrastructure pedestrian detection system, which includes image registration, background modeling, image filtering, candi-date generation, feature extraction, and classification. The paper explains the MATLAB implementation of the algorithm with a road-collected dataset and provides analysis for the detection results with respect to detection accuracy and runtime. The algorithm implementation results show an improvement in the detection performance and algorithm runtime. |
| Databáze: | OpenAIRE |
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