In this paper, the design of a quadrotor vehicle having a person-tracking and observation system, which uses human gesture recognition, is described. The system has three operating functions, namely, object tracking, ...
详细信息
In this paper, the design of a quadrotor vehicle having a person-tracking and observation system, which uses human gesture recognition, is described. The system has three operating functions, namely, object tracking, human gesture recognition, and fixed-point cruising. The tracking-learning-detection (TLD) algorithm was used to enable the autonomous tracking of the object from images. An extended Kalman filter (EKF) provides an estimate of the current position of the quadrotor vehicle, and a fuzzy-proportional integral derivative (PID) controller provides position error compensation. The principle of the human gesture recognition system is as follows. A background model is first built from images using a Gaussian mixture model (GMM) to detect the foreground image. A nonlinear support vector machine (SVM) is then employed to recognize changes of gesture and establish interactivity between the vehicle and the user. The coordinates of the vehicle are marked using a GPS for fixed-point cruising. The coordinates and parameters of the points are set so that the quadrotor vehicle can follow them during cruising. Lastly, all of the functions are incorporated into the person-tracking and gesture-recognition system in the quadrotor. The experimental results show the feasibility of the above-mentioned methods, which can help us easily recognize the various gestures in this study.
Existing human eye tracking research is based on the off-line sample training cascade classifier and the traditional tracking algorithm. However, it is difficult to adapt to situations where human eye is partially blo...
详细信息
ISBN:
(纸本)9781538666142
Existing human eye tracking research is based on the off-line sample training cascade classifier and the traditional tracking algorithm. However, it is difficult to adapt to situations where human eye is partially blocked, gets morphological changes or scale changes and so on. In order to solve these problems, the tracking-learning-detection algorithm for online single-target long-term tracking is used and improved. A novel eye tracking method-the human eye tracking-learning-detection algorithm with tracking feedback is proposed. The detection area is adjusted adaptively and narrowed by the tracking feedback. Above problems are solved, the interference of similar targets is avoided, and the speed of human eye tracking is enhanced. The experimental results show that the algorithm has high tracking accuracy and frames per second.
We present a novel semi-supervised algorithm for classification of hyperspectral data from remote sensors. Our method is inspired by the tracking-learning-detection (TLD) framework, originally applied for tracking obj...
详细信息
We present a novel semi-supervised algorithm for classification of hyperspectral data from remote sensors. Our method is inspired by the tracking-learning-detection (TLD) framework, originally applied for tracking objects in a video stream. TLD introduced the co-training approach called P-N learning, making use of two independent 'experts' (or learners) that scored samples in different feature spaces. In a similar fashion, we formulated the hyperspectral classification task as a co-training problem, that can be solved with the P-N learning scheme. Our method uses both spatial and spectral features of data, extending a small set of initial labelled samples during the process of region growing. We show that this approach is stable and achieves very good accuracy even for small training sets. We analyse the algorithm's performance on several publicly available hyperspectral data sets. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
There are many works with many progresses using RGB-D on object tracking when long-term occlusion occurs. However, object tracking needs a higher requirement on hardware, like RGB-D cameras. To solve this problem, thi...
详细信息
ISBN:
(纸本)9781450363501
There are many works with many progresses using RGB-D on object tracking when long-term occlusion occurs. However, object tracking needs a higher requirement on hardware, like RGB-D cameras. To solve this problem, this paper proposes a novel depth information fusion tracker (DIFT) which handles occlusion by the following series of steps on general cameras. First, object occlusion is recognized during object detection. Second, a dynamic tracking model is established and updated according to whether the object is occluded. Third, the tracking model is refined by eliminating occluded regions. An extensive quantitative evaluation on public video sequences shows that the proposed method is robust and outperforms widely used trackers such as Kernelized Correlation Filter and tracking-learning-detection.
暂无评论