Investigations of multigrid algorithms for the estimation of optical flow fieldsin image sequences
Computer Vision, Graphics, and Image Processing
The statistics of optical flow
Computer Vision and Image Understanding
An active mesh based tracker for improved feature correspondences
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Making Good Features Track Better
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Point Matching under Large Image Deformations and Illumination Changes
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Multiple 3D object position estimation and tracking using double filtering on multi-core processor
Multimedia Tools and Applications
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The tracking speed and accuracy are two most important parameters for a target tracking system. In our study, the proposed target tracking algorithm combines the Harris method and the optical flow method. To improve the tracking speed, the Harris method is initially used to extract some target corner features, and the optical flow method is then used to more accurately match corner features for the subsequent video frames. When the tracked target is rotated or distorted, the barycenter algorithm is employed to compute the barycenter of those matched features of target. To meet the real-time-tracking requirement, a small-zone image searching method and a high speed digital signal processing system are also designed. Our experimental study shows that the method described in this paper has high accuracy of target tracking, and can be applied to the situations of rotated, distorted, and/or shielded targets, although it has a limitation that it is only suitable for smaller targets.