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In this paper, we propose a co-learning particle filter approach for vehicle tracking, which is very important for intelligent vehicle. The proposal distribution of the particle filter is a combination of an extra support vector machine (SVM) detector and the motion prior. Previous works focusing on how to online update the detector or the observation likelihood using the tracking results. These approaches...
In this paper, a semi-supervised particle filter approach is proposed for visual tracking. The combination of semi-supervised learning and particle filter is very natural since the unlabelled samples are generated by particle propagation. In addition, the proposed semi-supervised particle filter can online select different features for robust tracking. To the best knowledge of the authors, this is...
In this paper, we propose a semi-supervised ensemble tracking approach under the framework of particle filter. The particle filter is used not only for object searching, but also for unlabelled sample generation. By adopting the semi-supervised learning technology, these unlabelled samples which are generated online are utilized to progressively modify the classifier and make the ensemble tracker...
Detecting pedestrians in images is a challenging task, especially for the intelligent vehicle environment where there is a moving camera. In this paper, we develop a monocular vision based pedestrian detection system for intelligent vehicles. We propose a two-stage pedestrian detection approach. A full-body pedestrian detector with Haar-like wavelet features and cascade Adaboost classifier (P. Viola...
Face detection plays an important role in Computer Vision. In the past few years, much effort has been put into this field. Some approaches are based on Color Model which is highly expensive. Viola and Jones proposed a real-time approach based on AdaBoost, but it suffered from the risk of overfitting. In this paper, we propose a method to take advantage of Color Model and AdaBoost. A novel Face Region...
In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamic-inversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the DNF system,...
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