Tracking People through Occlusions
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
ViSE: Visual Search Engine Using Multiple Networked Cameras
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Recovering Non-overlapping Network Topology Using Far-field Vehicle Tracking Data
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Object matching in disjoint cameras using a color transfer approach
Machine Vision and Applications
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Object Inter-camera Tracking with Non-overlapping Views: A New Dynamic Approach
CRV '10 Proceedings of the 2010 Canadian Conference on Computer and Robot Vision
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we propose a novel approach to track multiple objects across non-overlapping cameras, which aims at giving each object a unique label during its appearance in the whole multi-camera system. We formulate the problem of the multiclass object recognition as a binary classification problem based on an AdaBoost classifier. As the illumination variance, viewpoint changes, and camera characteristic changes vary with camera pairs, appearance changes of objects across different camera pairs generally follow different patterns. Based on this fact, we use a categorical variable indicating the entry/exit cameras as a feature to deal with different patterns of appearance changes across cameras. For each labeled object, an adaptive model describing the intraclass similarity is computed and integrated into a sequence based matching framework, depending on which the final matching decisions are made. Multiple experiments are performed on different datasets. Experimental results demonstrate the effectiveness of the proposed method.