Range Image Based Classification System Using Support Vector Machines
Computational Intelligence and Security
Automatic Database Creation and Object's Model Learning
Knowledge Acquisition: Approaches, Algorithms and Applications
Computer Vision and Image Understanding
Boosted online learning for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
MAPACo-training: a novel online learning algorithm of behavior models
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
On-line multi-view forests for tracking
Proceedings of the 32nd DAGM conference on Pattern recognition
ICSR'10 Proceedings of the Second international conference on Social robotics
Unsupervised moving object detection with on-line generalized hough transform
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Online multiple instance boosting for object detection
Neurocomputing
A novel approach on silhouette based human motion analysis for gait recognition
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
On-line inverse multiple instance boosting for classifier grids
Pattern Recognition Letters
Incremental threshold learning for classifier selection
Neurocomputing
Online crowdsourcing subjective image quality assessment
Proceedings of the 20th ACM international conference on Multimedia
Segmentation based particle filtering for real-time 2d object tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
A data-driven detection optimization framework
Neurocomputing
Segmentation-based tracking by support fusion
Computer Vision and Image Understanding
Hough-based tracking of non-rigid objects
Computer Vision and Image Understanding
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Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for training such a classifier. Moreover, once trained, the boosted classifier cannot adjust to the particular scenario in which it is employed. In this paper, we propose a co-training based approach to continuously label incoming data and use it for online update of the boosted classifier that was initially trained from a small labeled example set. The main contribution of our approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The features used for classification are derived from Principal Component Analysis of the appearance templates of the training examples. In order to speed up the classification, background modeling is used to prune away stationary regions in an image. Our experiments indicate that starting from a classifier trained on a small training set, significant performance gains can be made through on-line updation from the unlabeled data.