Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
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
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pedestrian Detection via Periodic Motion Analysis
International Journal of Computer Vision
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
AdaBoost learning for human detection based on histograms of oriented gradients
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection
Engineering Applications of Artificial Intelligence
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In this work, we propose an algorithm of combining Histograms of Oriented Gradients(HOGs) with shape of head for human detection from a non-static camera. We use AdaBoost algorithm to learn local characteristics of human based on HOGs. Since local feature is easily affected by complex backgrounds and noise, the idea of this work is to incorporate the global feature for improving the detection accuracy. Here, we adopt the head contour as the global feature. The score for evaluating the existence of the head contour is through the Chamfer distance. Furthermore, the matching distributions of the head and non-head are modeled by Gaussian and Anova distributions, respectively. The combination of the human detector based on local features and head contour is achieved through the adjustment of the hyperplane of support vector machine. In the experiments, we exhibit that our proposed human detection method not only has higher detection rate but also lower false positive rate in comparison with the state-of-the-art human detector.