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Pedestrian recognition on embedded systems is a challenging problem since accurate recognition requires extensive computation. To achieve real-time pedestrian recognition on embedded systems, we propose hardware architecture suitable for HOG feature extraction, which is a popular method for high-accuracy pedestrian recognition. To reduce computational(More)
Robust and rapid object detection is one of the great challenges in the field of computer vision. This paper proposes a hardware architecture suitable for object detection by Viola and Jones based on an AdaBoost learning algorithm with Haar-like features as weak classifiers. Our architecture realizes rapid and robust detection with two major features:(More)
Co-occurrence histograms of oriented gradients (CoHOG) is a powerful feature descriptor for pedestrian detection. However, its calculation cost is large because the feature vector for the CoHOG descriptor is very high-dimensional. In this paper, in order to achieve real-time detection on embedded systems, we propose a novel hardware architecture for the(More)
One of the most frequently used operations in image recognition is morphological processing. In this paper, we propose a parallel implementation of morphological processing optimized for cell broadband engine (cell), which is one of the latest high performance embedded processors. By utilizing the computational power of cell suitable for image recognition,(More)
Nowadays, pedestrian recognition for automotive and security applications that require accurate recognition in images taken from distant observation points is a recent challenging problem in the field of computer vision. To achieve accurate recognition, both detection and tracking must be precise. For detection, some excellent schemes suitable for(More)
Recently, many researchers tackle accurate object recognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, cascade particle filter, is proposed to fill such demands in real(More)
In pedestrian detection, as sophisticated feature descriptors are used for improving detection accuracy, its processing speed becomes a critical issue. In this paper, we propose a novel speed-up scheme based on multiple-instance pruning (MIP), one of the soft cascade methods, to enhance the processing speed of support vector machine (SVM) classifiers. Our(More)