Paper
27 February 2015 Localizing people in crosswalks with a moving handheld camera: proof of concept
Marc Lalonde, Claude Chapdelaine, Samuel Foucher
Author Affiliations +
Proceedings Volume 9405, Image Processing: Machine Vision Applications VIII; 94050Y (2015) https://doi.org/10.1117/12.2074960
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
Abstract
Although people or object tracking in uncontrolled environments has been acknowledged in the literature, the accurate localization of a subject with respect to a reference ground plane remains a major issue. This study describes an early prototype for the tracking and localization of pedestrians with a handheld camera. One application envisioned here is to analyze the trajectories of blind people going across long crosswalks when following different audio signals as a guide. This kind of study is generally conducted manually with an observer following a subject and logging his/her current position at regular time intervals with respect to a white grid painted on the ground. This study aims at automating the manual logging activity: with a marker attached to the subject’s foot, a video of the crossing is recorded by a person following the subject, and a semi-automatic tool analyzes the video and estimates the trajectory of the marker with respect to the painted markings. Challenges include robustness to variations to lighting conditions (shadows, etc.), occlusions, and changes in camera viewpoint. Results are promising when compared to GNSS measurements.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marc Lalonde, Claude Chapdelaine, and Samuel Foucher "Localizing people in crosswalks with a moving handheld camera: proof of concept", Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050Y (27 February 2015); https://doi.org/10.1117/12.2074960
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KEYWORDS
Cameras

Video

Satellite navigation systems

Particles

Roads

Motion models

Automatic tracking

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