Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient learning with virtual threshold gates
Information and Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Pre-Attentive and Attentive Detection of Humans in Wide-Field Scenes
International Journal of Computer Vision
Automatic Database Creation and Object's Model Learning
Knowledge Acquisition: Approaches, Algorithms and Applications
Computer Vision and Image Understanding
Learning-based object tracking using boosted features and appearance-adaptive models
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
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
Supervised and semi-supervised online boosting tree for industrial machine vision application
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
A new framework for on-line object tracking based on SURF
Pattern Recognition Letters
Separable linear classifiers for online learning in appearance based object detection
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Conservative visual learning for object detection with minimal hand labeling effort
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
On-line inverse multiple instance boosting for classifier grids
Pattern Recognition Letters
Exploiting publicly available cartographic resources for aerial image analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Model probability in self-organising maps
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the classifier offline with manually labeled training data. We present a framework that learns the classifier online with automatically labeled data for the specific case of detecting moving objects from video. Motion information is used to automatically label training examples collected directly from the live detection task video. An online learner based on the Winnow algorithm incrementally trains a taskspecific classifier with these examples. Since learning occurs online and without manual help, it can continue in parallel with detection and adapt the classifier over time. The framework is demonstrated on a person detection task for an office corridor scene. In this task, we use background subtraction to automatically label training examples. After the initial manual effort of implementing the labeling method, the framework runs by itself on the scene video stream to gradually train an accurate detector.