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We propose a robust real-time person detection system, which aims to serve as solid foundation for developing solutions at an elevated level of reliability. Our belief is that clever handling of input data correlated with efficacious training algorithms are key for obtaining top performance. We introduce a comprehensive training method based on random sampling that compiles optimal classifiers with...
Face detection is widely used in interactive user interfaces and plays a very important role in the field of computer vision. In order to build a fully automated system that can analyze the information in face image, there is a need for robust and efficient face detection algorithms. One of the fastest and most successful approaches in this field is to use Haar-like features for facial appearance...
Face detection is a research area in computer vision of great interest. Even though several different methods have been developed, improvements can still be made in the false-positive detection and increase in the speed of the detector. In this work, we investigate the AdaBoost technique as an artificial neural network. We propose a new model called MLPBoost, which is an hybridization between AdaBoost...
AdaBoost based training method has become a state-of-the-art boosting approach in face detection system. In this paper, compared to the naive AdaBoost method, Forward Feature Selection (FFS) method is used in feature selection to reduce the training time by about 50 to 100 times without loss of performance. Furthermore, hierarchical feature spaces (both local and global) to construct a detector cascade...
Efficiently and accurately detecting pedestrians plays a crucial role in many vision applications such as video surveillance, multimedia retrieval and smart car etc. In order to find the right feature for this task, we first present a comprehensive experimental study on pedestrian detection using state-of-the-art locally-extracted features. Building upon our findings, we propose a new, simpler pedestrian...
In this paper, a robust and effective face detection method with HTF-Boosting is proposed. Firstly, a new feature, called Haar texture feature, is proposed that has many merits compared with Haar-Like feature. Secondly, a new Boosting algorithm, called Haar Texture Feature Boosting (HTF-Boosting), is proposed to construct strong face/nonface classifiers. The HTF-Boosting algorithm trains strong classifiers...
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