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
Robust Real-Time Face Detection
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
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Haar Wavelets and Edge Orientation Histograms for On---Board Pedestrian Detection
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Pedestrian Detection: An Evaluation of the State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Human detection is an important task in many applications such as intelligent transport systems, surveillance systems, automatic human assistance systems, image retrieval, and so on. This paper proposes a multiple scale of cell based Histogram of Oriented Gradients (HOG) features description for human detection system. Using these proposed feature descriptors, a robust system is developed according to decision tree structure of boosting algorithm. In this system, the integral image based method is utilized to compute feature descriptors rapidly, and then cascade classifiers are taken into account to reduce computational cost. The experiments were performed on INRIA's database and our own database, which includes samples in several different sizes. The experiment results showed that our proposed method produce high performance with lower false positive and higher recall rate than the standard HOG features description. This method is also efficient with different resolution and gesture poses under a variety of backgrounds, lighting, as well as individual human in crowds, and partial occlusions.