Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Space-Time Behavior Based Correlation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Sparse Features in Granular Space for Multi-View Face Detection
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Scene Aware Detection and Block Assignment Tracking in crowded scenes
Image and Vision Computing
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In this paper, we aim to detect human in video over large viewpoint changes which is very challenging due to the diversity of human appearance and motion from a wide spread of viewpoint domain compared with a common frontal viewpoint. We propose 1) a new feature called Intra-frame and Inter-frame Comparison Feature to combine both appearance and motion information, 2) an Enhanced Multiple Clusters Boost algorithm to co-cluster the samples of various viewpoints and discriminative features automatically and 3) a Multiple Video Sampling strategy to make the approach robust to human motion and frame rate changes. Due to the large amount of samples and features, we propose a two-stage tree structure detector, using only appearance in the 1st stage and both appearance and motion in the 2nd stage. Our approach is evaluated on some challenging Real-world scenes, PETS2007 dataset, ETHZ dataset and our own collected videos, which demonstrate the effectiveness and efficiency of our approach.