西北师大计算机论文(教授)

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on distance transform to the hand contour, and performs the palm tracking with a Kalman filter and the fingertip tracking with particle filters. Frati [8] tracks the hand and fingertips in depth space only based on 2D hand contour processing, followed by a Kalman filtering. This way makes the results unstable and sensitive to the depth ranges, and it is prone to fail with the closer (especially white-like) backgrounds. For tracking fingertips from the RGB-D image sequence, in this paper we need to simplify the scenario based on some basic assumptions: 1) the hand is closer to the camera compared to the other parts of human body. 2) the absolute errors within the regions of the forearm and hand is less than a pre-define threshold. 3) a palm is the biggest region for a hand, and sometimes the palm region is considered as a circle approximately. For most of the existing methods, the general scenario is that the palm is facing to the camera. But in our scenario, the back of the palm is facing to the camera, for example, playing the piano. In this paper, we propose a fingertip up/down movements tracking algorithm (3dFDT), and use these information as inputs for a human-computer interface. First, the ellipse model is applied to detect the hand skin regions. Then, the center region is considered as the center of the palm. Next, the fingertips are positioned using the convex defect detection from the hand contours. Furthermore, the depth information is aligned to the fingertips in the RGB color spaces. Finally, the sliding window strategy is employed to stabilize the depth information due to the noisy depth information.
(1. School of Electronics and Information, Northwestern Polytechnical University, Xian 710072; 2. School of Aeronautics, Northwestern Polytechnical University, Xian 710072; 3. School of Automation, Northwestern Polytechnical University, Xian 710072) Abstract: In this paper, we propose a 3D finger detecting and tracking algorithm (3dFDT) based on a sliding window for RGB-D sequences. Microsoft Kinect is utilized as a 3D depth camera, which provides RGB images and depth data simultaneously. However the depth data have large noises when the background is white. We are focusing on tracking the up or down movements of the fingertips in order to apply in some applications such as playing the piano on the table or “in the air”. First, the skin statistical ellipse model is applied to detect the hand skin region, and the pseudo-contours are removed to get the interested hand region. Then, the region center is considered as the center of the palm. Next, the fingertips are located precisely using the convex defect detection based on the refined hand contours. Furthermore, the depth information is aligned to the fingertips in the RGB channels. Finally, the sliding window strategy is employed to stabilize the depth information due to large noises from the depth space especially when dealing with the white surface as backgrounds. The experimental results show that our proposed method is effective, and it can be applied in the real-time applications for non-contact interactions. Key words: Image Processing; Fingertips Tracking; RGB-D images; sliding window; Kinect.
Foundations: This work is supported by the National Natural Science Foundation of China (No.61103062), the Research Fund for the Doctoral Program of Higher Education (No.20116102120031), the NPU Foundation for Fundamental Research (No.JC201249), the Graduate Starting Seed Fund for NPU (No.Z2014124), and Scientific Research Foundation for Returned Scholars, Ministry of Education of China (2013). Brief author introduction:WU Jun (1977-), Male, Associate Professor. His research interests are in machine learning, multimedia information retrieval, social media analysis, and human-computer interface. E-mail: junwu@
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A 3D Fingertips Detecting and Tracking Algorithm based on the SliAO Wenjing1, KUANG Cailiang1, LIU Zhenbao2, BU Shuhui2, HAN Junwei3*
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0 Introduction
With the rapid development of the HCI interaction and virtual reality, the people prefer to the natural and friendly interaction technologies, such as speech recognition [1] and gesture recognition [2] to control the computers, rather than the traditional keyboard and mouse It gives us perfect experiences to talk or communicate with computers. For the gesture recognition, the multi-touch technique is successful and popular in portable devices [3], and extracts much attentions. While, it need to touch within a 2D plane. In some applications, we need non-contact gesture interaction in a 3D space.. In the early stage, binocular vision system is widely used to get the depth information [4]. While the calibration is necessary before computing. In June 2010, Microsoft released a RGB-D depth camera called Kinect XBOX360. With this type of depth camera, we can easily capture the 3D information for body parts such as the hands. But for tiny object tracking, such as finger tracking, the noises in depth space make it not trivial. Gesture recognition based on RGB-D image sequences mainly use the depth information to detect the hand and the fingers. Maisto and Panella [5] first build a matrix with depth information; then, for every pixel of the image, its distance from the hand-point along the three axes is checked: if it is less than a threshold, the point will be identified as belonging to the hand. Raheja et al [6] find the palm using the image circular filter, obtain the depth image of the fingers, and locate the fingertips through searching the minimal depth of each fingertips. Liang [7] locates the palm based