Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
Personnel tracking on construction sites using video cameras
Advanced Engineering Informatics
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel covariance image region description for object tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Preface: Special issue on construction informatics
Advanced Engineering Informatics
A performance evaluation of vision and radio frequency tracking methods for interacting workforce
Advanced Engineering Informatics
Advanced Engineering Informatics
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This paper proposes a tracking scheme for tracking multiple workers on construction sites using video cameras. Prior work has compared several contemporary tracking algorithms on construction sites and identified several difficulties, one of which included the existence of interacting workforce. In order to address the challenge of multiple workers within the camera's field of view, the authors have developed a tracking algorithm based upon machine learning methods. The algorithm requires several sample templates of the tracking target and learns a general model that can be applied to other targets with similar geometry. A parameterized feature bank is proposed to handle the case of variable appearance content. The tracking initialization has been discussed for different types of video cameras. A multiple tracking management module is applied to optimize the system. The principal objective of this paper is to test and demonstrate the feasibility of tracking multiple workers from statically placed and dynamically moving cameras.