Enhanced adaptive bandwidth tracking using mean shift algorithm

Mohammadi, Seyed Amir; Amoozegar, Sana; Jolfaei, Alireza; Mirghadri, Abdolrasoul

doi:10.1109/iccsn.2011.6014773

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…Mean shift is a nonparametric mode seeking algorithm [1,2], which iteratively locates the modes in the data by maximizing the kernel density estimate. As with its the nonparametric nature, the mean shift algorithm becomes a powerful tool to mode-seeking and clustering [3,4], and it has also been applied to solve several computer vision problems, e.g., image filtering [1], segmentation [5,6,7], visual tracking [8,9,10,11,12] and action recognition [13,14].…”

Section: Introductionmentioning

confidence: 99%

A bidirectional adaptive bandwidth mean shift strategy for clustering

Meng

Liu

Liang

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

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The bandwidth of a kernel function is a crucial parameter in the mean shift algorithm. This paper proposes a novel adaptive bandwidth strategy which contains three main contributions. (1) The differences among different adaptive bandwidth are analyzed. (2) A new mean shift vector based on bidirectional adaptive bandwidth is defined, which combines the advantages of different adaptive bandwidth strategies. (3) A bidirectional adaptive bandwidth mean shift (BAMS) strategy is proposed to improve the ability to escape from the local maximum density. Compared with contemporary adaptive bandwidth mean shift strategies, experiments demonstrate the effectiveness of the proposed strategy.Index Terms-Clustering, Mean Shift, Bidirectional Adaptive Bandwidth.2. The BAMS strategy combining the advantages of different AMS strategies is proposed to improve the ability to escape from the local maximum destiny.3. Experimental comparisons on a synthetic dataset and a benchmark dataset, i.e. BSD500 image dataset [21],

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Section: Introductionmentioning

confidence: 99%

A bidirectional adaptive bandwidth mean shift strategy for clustering

Meng

Liu

Liang

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

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“…The CBWH scheme significantly reduces the background interference in a target area by enhancing prominent features of a target model and reducing the impact of similar image features shared by the target and background. In [3], an algorithm is proposed based on CBWH. The algorithm performs well when targets become smaller.…”

Section: Introductionmentioning

confidence: 99%

A Scale Adaptive Mean-Shift Tracking Algorithm for Robot Vision

Kang

Xie

2013

Advances in Mechanical Engineering

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The Mean-Shift (MS) tracking algorithm is an efficient tracking algorithm. However, it does not work very well when the scale of a tracking target changes, or targets are occluded in the movements. In this paper, we propose a scale-adaptive Mean-Shift tracking algorithm (SAMSHIFT) to solve these problems. In SAMSHIFT, the corner matching is employed to calculate the affine structure between adjacent frames. The scaling factors are obtained based on the affine structure. Three target candidates, generated by the affine transformation, the Mean Shift and the Mean Shift with resizing by the scaling factors, respectively, are applied in each iteration to improve the tracking performance. By selecting the best candidate among the three, we can effectively improve the scale adaption and the robustness to occlusion. We have evaluated our algorithm in a PC and a mobile robot. The experimental results show that SAMSHIFT is well adaptive to scale changing and robust to partial occlusion, and the tracking speed is fast enough for real-time tracking applications in robot vision.

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Binary cuckoo search metaheuristic-based supercomputing framework for human behavior analysis in smart home

Kaur

Sharma

et al. 2019

J Supercomput

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Enhanced adaptive bandwidth tracking using mean shift algorithm

Cited by 8 publications

References 4 publications

A bidirectional adaptive bandwidth mean shift strategy for clustering

A bidirectional adaptive bandwidth mean shift strategy for clustering

A Scale Adaptive Mean-Shift Tracking Algorithm for Robot Vision

Binary cuckoo search metaheuristic-based supercomputing framework for human behavior analysis in smart home

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