By Miguel Lourenço, João Pedro Barreto (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed court cases of the twelfth ecu convention on computing device imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers awarded have been rigorously reviewed and chosen from 1437 submissions. The papers are geared up in topical sections on geometry, 2nd and 3D form, 3D reconstruction, visible attractiveness and category, visible positive factors and picture matching, visible tracking: motion and actions, types, optimisation, studying, visible monitoring and picture registration, photometry: lighting fixtures and color, and snapshot segmentation.
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Additional info for Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV
Acknowledgments. The authors acknowledge the Portuguese Science Foundation (FCT) that generously funded this work through grants PTDC/EIACCO/109120/2008 and SFRH/BD/63118/2009. uk/rbf/CAVIARDATA1/ 14 M. P. Barreto References 1. : An Iterative Image Registration Technique with an Application to Stereo Vision. In: DARPA Image Understanding Workshop, pp. 121–130 (1981) 2. : Good features to track. In: IEEE-CVPR, pp. 593–600 (1994) 3. : Object Tracking: A survey. ACM Comput. Surv. 38 (2006) 4. : Visual Modeling with a Hand-Held Camera.
Motion Model: To achieve a robust state motion transition p(xk |xk−1 , mk = i), we simply adopt two motion models (zero- and first-order motion model) in terms of (X,Y) translation. The zero-order motion is identical to the random walk motion. The firstorder motion utilizes the prior information of (X,Y) translation that is simply obtained by computing the difference between estimated X and Y positions at k − 1 and k − 2. More efficient usage of multiple motion models for visual tracking is referred to .
The first focuses on flexibility and is computed based on recent object appearances that reflect an instantaneous object appearance. , a set of representative appearance templates . Due to the reconstructed appearance, we can measure robustly each tracker’s reliability, although occlusion or outliers exist in the object’s appearance. Hence, the latter appearance model is more stable and conservative than the former. These reference properties are maintained via the proposed learning strategy.