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In this work, a pedestrian detection method based on adaptive boosting is proposed. The proposed method works on still images. The features utilized in the work are derived from Haar-like templates. An Adaboost classifier is utilized for both feature selection and classification. To show the effectiveness of the proposed algorithm, the system is trained by using Nicta Pedestrian Dataset and tested...
In this paper we propose a hardware solution by the use of FPGA based circuit for real time face detection. We have built a sub-window architecture for the extraction of Haar-like features, which are the basic elements of weak classifiers according to AdaBoost learning algorithm. The main contribution is that the proposed architecture removes traditional frame buffer, and only reserve the line buffer...
A novel eye corner detection method is presented in this paper. The method is capable of detecting a pair of inner and outer eye corners from a complex background. Faces and eyes are first detected from the image by Adaboost with Haar-like features. Next, the potential area of the eye corner is determined based on variance projection function. In view of the corner characteristics of the eye corner,...
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...
Present elevator control use button sensors to determine when and where to dispatch an elevator car, which donpsilat use the number of passengers. In this paper, we analyze images from camera to detect how many persons waiting for the elevator or in an elevator. A novel framework is proposed for optimized elevator schedule. Extended Haar-like features and Adaboost are used to train a head-shoulder...
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