Abstract
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.
Highlights
Between 2010 and 2013 the number of registered vehicles increased by 16% [1]
This paper focuses on the image processing and machine learning algorithms that needs to be implemented in the infrastructure for accurate detection results
Harris-Stephens approach for corner detection is used for image registration and matching
Summary
Between 2010 and 2013 the number of registered vehicles increased by 16% [1]. This causes a significant increase in the number of road accidents and road fatalities. The flat world approach for candidate generation assumes the world is flat, and it generates the candidates from the ground plane level [3] This approach provides inaccurate results when the camera location changes with respect to the ground because of vehicle dynamics and road slope. The current on-board candidate generation approaches can’t distinguish between static and moving objects in an image This leads to the generation of many unnecessary candidates, which can cause false detection and increases the algorithm runtime. An example of this is generating candidates for trees and buildings in an image and misclassifying them as pedestrians.
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More From: International Journal of Advanced Computer Science and Applications
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