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Compared with the traditional interaction approaches, such as keyboard, mouse, pen, etc, vision based hand interaction is more natural and efficient. In this paper, we proposed a robust real-time hand gesture recognition method. In our method, firstly, a specific gesture is required to trigger the hand detection followed by tracking; then hand is segmented(More)
As an emerging human-computer interaction ap-proachvision based hand interaction is more natural and efficient. Howeverin order to achieve high accuracy , most of the existing hand posture recognition methods need a large number of labeled samples which is expensive or unavailable in practice. In this paper , a co-training based method is proposed to(More)
Selective sampling has been widely used in relevance feedback of image retrieval to alleviate the burden of labeling by selecting the most informative instances for user to label. Traditional sample selection scheme often selects a batch of instances each time and label them simultaneously, which ignores the correlation among instances and results in(More)
—Behavior analysis across multi-cameras becomes more and more popular with the rapid development of camera network in video surveillance. In this paper, we propose a novel unsupervised graph matching framework to associate trajectories across partially overlapping cameras. Firstly, trajectory extraction is based on object extraction and tracking and is(More)
Person re-identification is one of the most critical tasks in the field of computer vision and has widely applications for abnormal detection and object retrieval in video surveillance. In this paper, we give an extensive comparison for different kinds of visual features including hand-craft features and Convolutional Neural Networks (CNN) features. We run(More)
Crowd counting is a key problem for many computer vision tasks while most existing methods try to count people based on regression with hand-crafted features. Recently, the fast development of deep learning has resulted in many promising detectors of generic object classes. In this paper, to effective leverage the discriminability of convolutional neural(More)