Machine Learning
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
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
Inter-camera association of multi-target tracks by on-line learned appearance affinity models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Continuous tracking within and across camera streams
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Person re-identification by probabilistic relative distance comparison
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We propose a novel framework for associating multi-target trajectories across multiple non-overlapping views (cameras) by constructing an invariant model per each observed target. Ideally, these models represent the targets in a unique manner. The models are constructed by generating synthetic images that simulate how targets would be seen from different viewpoints. Our framework does not require any training or other supervised phases. Also, we do not make use of spatiotemporal coordinates of trajectories, i.e., our framework seamlessly works with both overlapping and non-overlapping field-of-views (FOVs) as well as widely separated ones. Also, contrary to many other related works, we do not try to estimate the relationship between cameras that tends to be error prone in environments like airports or supermarkets where targets wander about different areas, stop at times, or turn back to their starting location. We show the results obtained by our framework on a rather challenging dataset. Also, we propose a black-box approach based on Support Vector Machine (SVM) for fusing multiple pertinent algorithms and demonstrate the added value of our framework with respect to some basic techniques.