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
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Multiple view geometry in computer vision
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Experimental comparisons of online and batch versions of bagging and boosting
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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Robust Real-Time Face Detection
International Journal of Computer Vision
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Distinctive Image Features from Scale-Invariant Keypoints
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Matching with PROSAC " Progressive Sample Consensus
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Online Selection of Discriminative Tracking Features
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Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Semi-supervised On-Line Boosting for Robust Tracking
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IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavior recognition from video based on human constrained descriptor and adaptable neural networks
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Neurocomputing
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We present a new object tracking scheme by employing adaptive classifiers to match the corresponding keypoints between consecutive frames. The detection of interest points is a critical step in obtaining robust local descriptions. This paper proposes an efficient feature detector based on SURF, by incrementally predicting the search space, to enhance the repeatability of the tracked interest points. Instead of computing the SURF descriptor, we construct a classifier-based descriptor using on-line boosting. With on-line learning ability based on our sample weighting mechanism, the classifier maintains its discriminative power to establish robust feature description and reliable points matching for subsequent tracking. In addition, matching candidates are validated using improved RANSAC to ensure correct updates and accurate tracking. All of these ingredients contribute measurably to improving overall tracking performance. Experimental results demonstrate the robustness and accuracy of our proposed technique.