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A novel algorithm is proposed to perform object tracking with multiple cameras in the Bayesian Inference framework. The key contribution is the exploitation of Bayesian network to fuse spatial-temporal position and object template feature in multiple cameras. Firstly, Bayesian network is used to model the multiple static cameras' tracking system. Then, the high-dimensional joint posterior is propagated...
The tracking robustness can be improved greatly using multi-camera system because the overlapping field of view provides more information of the objects. However, traditional template matching based multi-camera object tracking suffers from the tracking errors brought by view point differences. To solve the problem, we propose the extended online HECOL (Homography and Epipolar-based Consistent Labeling)...
We describe a 3-D object reconstruction method using a stationary camera and a planar mirror. No external calibration is required. The mirror provides the extra views needed for a multiple-view reconstruction. We examine the imaging geometry of the camera-mirror setup and prove a theorem that gives us the constraints for computing the orientation of the mirror. The constraints are derived from the...
We propose a complete solution to robust and accurate object tracking in face of various types of occlusions which pose many challenges to correct judgment of occlusion situation and proper update of target template. In order to tackle those challenges, we first propose a content-adaptive progressive occlusion analysis (CAPOA) algorithm. By combining the information provided by spatiotemporal context,...
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