scholarly journals Target Tracking Based on Mean Shift and KALMAN Filter with Kernel Histogram Filtering

2011 ◽  
Vol 4 (2) ◽  
Author(s):  
Sara Qazvini Abhari ◽  
Towhid Zargar Ershadi
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Mean Shift

Target Scale Adaptation Based on Corner Registration

2014 ◽  
Vol 556-562 ◽  
pp. 4260-4263
Author(s):  
Bing Yun Dai ◽  
Hui Zhao ◽  
Zheng Xi Kang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Distance Ratio ◽  
Scale Adaptation ◽  
Mean Shift Algorithm

Target tracking algorithm mean-shift and kalman filter does well in tracking target. However, mean-shift algorithm may not do well in tracking the target which the size of target is changing gradually. Although some scholars put forward by 10% of the positive and negative incremental to scale adaptive,the algorithm can not be applied to track the target which gradually becomes bigger. In this paper, we propose registration corners of the target of the two adjacent frames, then calculate the distance ratio of registration corners.Use the distance ratio to determine the target becomes larger or smaller. The experimental results demonstrate that the proposed method performs better compared with the recent algorithms.

Target Tracking Analysis Based on Corner Registration

2013 ◽  
Vol 760-762 ◽  
pp. 1997-2001
Author(s):  
Zheng Xi Kang ◽  
Hui Zhao ◽  
Yuan Zhen Dang
Keyword(s):  
Kalman Filter ◽  
Theoretical Analysis ◽  
Target Tracking ◽  
Affine Transformation ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Transformation Theory ◽  
Mobile Target ◽  
The Mean ◽  
Initial Iteration

Target tracking algorithm based on Mean-Shift and Kalman filter does well in linear tracking. However, the algorithm might lose the target when the trace of mobile target is curve or the acceleration is not constant. To cope with these drawbacks, this paper proposes Target Tracking Analysis Based on Corner Registration. The algorithm modifies the initial iteration center of Mean-Shift by using the corner features combined with affine transformation theory and then the Mean-Shift can track the target. The theoretical analysis and the experimental results demonstrate that this method can overcome the drawbacks we talk above and make achievements in target tracking.

Target tracking based on improved Mean-Shift and adaptive Kalman filter

2010 ◽  
Vol 30 (6) ◽  
pp. 1573-1576 ◽  
Author(s):  
Shang-bo ZHOU ◽  
Peng HU ◽  
Yu-jiong LIU
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Mean Shift ◽  
Adaptive Kalman Filter

Algorithm of target tracking based on Mean-Shift with adaptive bandwidth of kernel function

2009 ◽  
Vol 29 (6) ◽  
pp. 1680-1682
Author(s):  
Chang-tao CHEN ◽  
Qin ZHU ◽  
Sheng-yi ZHOU ◽  
Jia-ming ZHANG
Keyword(s):  
Target Tracking ◽  
Kernel Function ◽  
Mean Shift ◽  
Adaptive Bandwidth
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