The nature of statistical learning theory
The nature of statistical learning theory
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Robust Real-Time Face Detection
International Journal of Computer Vision
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
AdaBoost face detection on the gpu using Haar-like features
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Face and marker detection using Gabor frames on GPUs
Signal Processing
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Nowadays, the use of machine learning methods for visual object detection has become widespread. Those methods are robust. They require an important processing power and a high memory bandwidth which becomes a handicap for real-time applications. The recent evolution of commodity PC computer graphics boards (GPU) has the potential to accelerate those algorithms. In this paper, we present a novel use of graphics hardware for object detection in advanced computer vision applications. We implement a system for object-detection based on AdaBoost [1]. This system can be tuned to run partially or totally on the GPU. This system is evaluated with two face-detection applications. Those applications are based on the boosted cascade of classifiers: Multiple Layers Face Detection (MLFD), and Single Layer Face Detection (SLFD). We show that the SLFD implementation on GPU performs up to nine times faster than its CPU counterpart. The MLFD, in the other hand, can be accelerated using the (GPU) and performs up to three times faster than the CPU. To the best of our knowledge, this is the first attempt to implement a sliding window technique for visual object-detection on GPU, with promessing performance.