A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
A Pose-Invariant Descriptor for Human Detection and Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
International Journal of Computer Vision
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Combination of Feature Extraction Methods for SVM Pedestrian Detection
IEEE Transactions on Intelligent Transportation Systems
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
IEEE Transactions on Image Processing
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
IEEE Transactions on Circuits and Systems for Video Technology
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During the last decade, various successful human detection methods have been developed. However, most of these methods focus on finding powerful features or classifiers to obtain high detection rate. In this work we introduce a pedestrian detection and tracking system to extract and track human objectives using an on board monocular camera. The system is composed of three stages. A pedestrian detector, which is based on the non-overlap HOG feature and an Oriented LBP feature, is applied to find possible locations of humans. Then an object validation step verifies detection results and rejects false positives by using a temporal coherence condition. Finally, Kalman filtering is used to track detected pedestrians. For a 320×240 image, the implementation of the proposed system runs at about 14 frames/second, while maintaining an human detection rate similar to existing methods.