A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Boosting Nested Cascade Detector for Multi-View Face Detection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Robust Object Detection via Soft Cascade
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online Selecting Discriminative Tracking Features Using Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
ACM Computing Surveys (CSUR)
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Sharing features between objects and their attributes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning to share visual appearance for multiclass object detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Efficient multi-camera detection, tracking, and identification using a shared set of haar-features
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Online domain adaptation of a pre-trained cascade of classifiers
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Evaluation of background subtraction techniques for video surveillance
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
IEEE Transactions on Image Processing
Vehicle matching in smart camera networks using image projection profiles at multiple instances
Image and Vision Computing
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This paper presents an integrated solution for the problem of detecting, tracking and identifying vehicles in a tunnel surveillance application, taking into account practical constraints including real-time operation, poor imaging conditions, and a decentralized architecture. Vehicles are followed through the tunnel by a network of non-overlapping cameras. They are detected and tracked in each camera and then identified, i.e. matched to any of the vehicles detected in the previous camera (s). To limit the computational load, we propose to reuse the same set of Haar-features for each of these steps. For the detection, we use an AdaBoost cascade. Here we introduce a composite confidence score, integrating information from all stages of the cascade. A subset of the features used for detection is then selected, optimizing for the identification problem. This results in a compact binary 'vehicle fingerprint', requiring minimal bandwidth. Finally, we show that the same subset of features can also be used effectively for tracking. This Haar-features based 'tracking-by-identification' yields surprisingly good results on standard datasets, without the need to update the model online. The general multi-camera framework is validated using three tunnel surveillance videos.