Coupled Visual and Kinematic Manifold Models for Tracking
International Journal of Computer Vision
Manifold topological multi-resolution analysis method
Pattern Recognition
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Human action segmentation and classification based on the Isomap algorithm
Multimedia Tools and Applications
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Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D trajectories and fully take advantage of the additional information available from the multiple sensors. Previous approaches to the "correspondence across views" problem include matching features, using camera calibration information, and computing homographies between views under the assumption that the world is planar. However, it can be difficult to match features across significantly different views. Furthermore, calibration information is not always available and planar world hypothesis can be too restrictive. In this paper, a new approach is presented for matching correspondences based on the use of nonlinear manifold learning and system dynamics identification. The proposed approach does not require similar views, calibration nor geometric assumptions of the 3D environment, and is robust to noise and occlusion. Experimental results demonstrate the use of this approach to generate and predict views in cases where identity labels become ambiguous.