Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
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
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Video-Based Driver Assistance—From Basic Functions to Applications
International Journal of Computer Vision
Video Coding: An Introduction to Standard Codecs
Video Coding: An Introduction to Standard Codecs
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selected Papers from the 4th Canadian Workshop on Information Theory and Applications II
Selected Papers from the 4th Canadian Workshop on Information Theory and Applications II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ACM SIGGRAPH 2006 Papers
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Automatic object extraction and reconstruction in active video
Pattern Recognition
VideoCut: Removing Irrelevant Frames by Discovering the Object of Interest
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Automatic Detection of Object of Interest and Tracking in Active Video
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Image trimming via saliency region detection and iterative feature matching
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
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We propose a novel method for automatic detection and tracking of Object of Interest (OOI) from actively acquired videos by non-calibrated cameras. The proposed approach benefits from the object-centered property of Active Video and facilitates self-initialization in tracking. We first use a color-saliency weighted Probability-of-Boundary (cPoB) map for keypoint filtering and salient region detection. Successive Classification and Refinement (SCR) is used for tracking between two consecutive frames. A strong classifier trained on-the-fly by AdaBoost is utilized for keypoint classification and subsequent Linear Programming solves a maximum similarity problem to reject outliers. Experiments demonstrate the importance of Active Video during the data collection phase and confirm that our new approach can automatically detect and reliably track OOI in videos.