AI Article Synopsis
- The paper presents a single-camera system designed to automatically detect wheelchairs and their movement direction, aimed at improving patient monitoring in healthcare settings.
- The proposed method uses a cascaded decision tree (CDT) integrated with boosted-cascade classifiers to efficiently distinguish wheelchairs from non-wheelchair objects and identify their direction quickly.
- Experiments demonstrated that this system achieves over 92% detection accuracy even in crowded environments, enhancing both detection speed and reliability.
Article Abstract
One of the major goals of healthcare systems is to automatically monitor patients of special needs and alarm the caregivers for providing assistant. In this paper, an efficient single-camera multidirectional wheelchair detector based on a cascaded decision tree (CDT) is proposed to detect a wheelchair and its moving direction simultaneously from video frames for a healthcare system. Our approach combines a decision tree structure and boosted-cascade classifiers to construct a new CDT that can perform early confidence decisions in a hierarchical manner to rapidly reject nonwheelchairs and decide the moving directions. We also impose the tracking history to guide detection routes in the CDT to further reduce detection time and increase detection accuracy. The experiments show over 92% detection rate under cluttered scenes.
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| http://dx.doi.org/10.1109/TITB.2009.2037618 | DOI Listing |
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