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The Research About Adaptive Active Recognition and Tracking Technology of Fast Target Image Strength 期刊论文
Ieee Sensors Journal, 2020, 卷号: 20, 期号: 20, 页码: 11795-11801
作者:  L. Ning,C. X. Liu,Y. F. Zhang,L. H. Cao and Z. B. Chen
收藏  |  浏览/下载：2/0  |  提交时间：2021/07/06
Biologically Visual Perceptual Model and Discriminative Model for Road Markings Detection and Recognition 期刊论文
Mathematical Problems in Engineering, 2018, 卷号: 0, 页码: 11
作者:  Jia, H. Q.;  Wei, Z. H.;  He, X.;  Lv, Y.;  He, D. L.
收藏  |  浏览/下载：4/0  |  提交时间：2019/09/17
A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS 期刊论文
Applied Sciences-Basel, 2017, 卷号: 7, 期号: 10
作者:  Lan, T. J.;  X. C. Xue;  J. L. Li;  C. S. Han and K. H. Long
收藏  |  浏览/下载：19/0  |  提交时间：2018/06/13
Remote sensing image target recognition based on fast retina key point local invariant feature 期刊论文
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2016, 卷号: 37, 期号: 4
作者:  Chen, Y.;  W. Xu;  Y. Piao and J. Chen
收藏  |  浏览/下载：21/0  |  提交时间：2017/09/11
智能视频监控系统中若干关键技术研究 学位论文
博士: 中国科学院大学, 2015
作者:  毕国玲
收藏  |  浏览/下载：160/0  |  提交时间：2015/11/30
Image deblurring with adaptive total variation model (EI CONFERENCE) 会议论文
International Conference on Image Processing and Pattern Recognition in Industrial Engineering, August 7, 2010 - August 8, 2010, Xi'an, China
Bai Y.; Ding Y.; Zhang X.; Jia H.; Guo L.
收藏  |  浏览/下载：9/0  |  提交时间：2013/03/25
Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, November 15, 2007 - November 17, 2007, Wuhan, China
Meng B.; Zhu M.; Han G.; Wu Z.
收藏  |  浏览/下载：21/0  |  提交时间：2013/03/25
A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently  we first use Sobel algorithm to extract the profile of the object. Then  we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones  in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise  the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here  we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.  

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