Pedestrian Tracking at Signalized Intersections Leveraging Multi-Camera Field of Views Using Covolutional Neural Network-Based Pose Estimation Algorithm
Publication: International Conference on Transportation and Development 2024
ABSTRACT
Pedestrian detection poses significant challenges for traffic safety researchers, given diverse motion patterns, clothing colors, partial occlusions, and varying positions concerning detection devices. Surveillance cameras, light detection and ranging (LiDAR), and microwaves beams have been utilized in pedestrians’ detection, leading to robust safety assessment methodologies. Despite their efficacy, widespread deployment encounters constraints. This study introduces an optimized approach by combining surveillance cameras with a distinctive convolutional neural network (CNN)-based pose estimation algorithm for precise pedestrian detection at signalized intersections. A geometrical spatial proximity method, grounded in linear and curvilinear perspectives, restores pedestrian joint coordinates from the image plane to a top-down view. These coordinates are clustered and integrated to construct pedestrian trajectories. The proposed framework serves as an efficient and accurate tool for assessing pedestrian motions, addressing challenges in widespread deployment and contributing significantly to advancing traffic safety practices at signalized intersections.
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International Conference on Transportation and Development 2024
Pages: 490 - 501
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Published online: Jun 13, 2024
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