Kernel-based object tracking via particle filter and mean shift algorithm

Chia, Y. S.; Kow, Wei Yeang; Khong, Wei Leong; Kiring, Aroland; Teo, Kenneth Tze Kin

doi:10.1109/his.2011.6122159

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…There is a volume of research that approves the accuracy and robustness of MSPF based hybrid tracking methods [22][23][24][25][26][27][28][29]. Shan Caifeng combined PF and MS to track a hand for an application of intelligent wheelchair.…”

Section: Methodsmentioning

confidence: 99%

Adaptive Framework for Multi-Feature Hybrid Object Tracking

2018

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Object tracking is a computer vision task deemed necessary for high-level intelligent decision-making algorithms. Researchers have merged different object tracking techniques and discovered a new class of hybrid algorithms that is based on embedding a meanshift (MS) optimization procedure into the particle filter (PF) (MSPF) to replace its inaccurate and expensive particle validation processes. The algorithm employs a combination of predetermined features, implicitly assuming that the background will not change. However, the assumption of fully specifying the background of the object may not often hold, especially in an uncontrolled environment. The first innovation of this research paper is the development of a dynamically adaptive multi-feature framework for MSPF (AMF-MSPF) in which features are ranked by a ranking module and the top features are selected on-the-fly. As a consequence, it improves local discrimination of the object from its immediate surroundings. It is also highly desirable to reduce the already complex framework of the MSPF to save resources to implement a feature ranking module. Thus, the second innovation of this research paper introduces a novel technique for the MS optimization method, which reduces its traditional complexity by an order of magnitude. The proposed AMF-MSPF framework is tested on different video datasets that exhibit challenging constraints. Experimental results have shown robustness, tracking accuracy and computational efficiency against these constraints. Comparison with existing methods has shown significant improvements in term of root mean square error (RMSE), false alarm rate (FAR), and F-SCORE.

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Section: Methodsmentioning

confidence: 99%

Adaptive Framework for Multi-Feature Hybrid Object Tracking

2018

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“…In other words, after that the object region is normalized in a unit circle, a monotonically decreasing kernel is masked on the circle, which shows the importance of each pixel in the circle. Chia et al [5] have also used kernel-based tracking. They applied Mean-Shift (MS) algorithm to particle filter sampling process in order to decrease the number of particles which reduces the computational time.…”

Section: Rel Ated Wor Kmentioning

confidence: 99%

Kernel-based object tracking using particle filter with incremental Bhattacharyya similarity

Dehshibi

Vafanezhad²,

Shanbehzadeh

2013

13th International Conference on Hybrid Intelligent Systems (HIS 2013)

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In this paper, we propose a method for kernel based object tracking in order to deal with partial occlusion.We use particle filter to estimate target position accurately.The incremental Bhattacharyya Dissimilarity (IBD) based stage is designed to consistently distinguish the particles located in the object region from the others placed in the background. While the target is occluded by background or

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“…K-means clustering [10] is selected to perform the clustering. Mean-shift algorithm for the clustering is opted to be used for the clustering algorithm [11]. However, because of the mean-shift clustering performs much slower than the k-means although the value k are not needed as input parameter, mean-shift clustering is not selected for the clustering on the computed eigenvector.…”

Section: Multistage Image Segmentation With Normalised Cutsmentioning

confidence: 99%

Graph-Based Image Segmentation Using K-Means Clustering and Normalised Cuts

Choong

Khong

Kow

et al. 2012

2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks

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Image segmentation with low computational burden has been highly regarded as important goal for researchers. Various image segmentation methods are widely discussed and more noble segmentation methods are expected to be developed when there is rapid demand from the emerging machine vision field. One of the popular image segmentation methods is by using normalised cuts algorithm. It is unfavourable for a high resolution image to have its resolution reduced as high detail information is not fully made used when critical objects with weak edges is coarsened undesirably after its resolution reduced. Thus, a graph-based image segmentation method done in multistage manner is proposed here. In this paper, an experimental study based on the method is conducted. This study shows an alternative approach on the segmentation method using k-means clustering and normalised cuts in multistage manner.

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Kernel-based object tracking via particle filter and mean shift algorithm

Cited by 14 publications

References 11 publications

Adaptive Framework for Multi-Feature Hybrid Object Tracking

Adaptive Framework for Multi-Feature Hybrid Object Tracking

Kernel-based object tracking using particle filter with incremental Bhattacharyya similarity

Graph-Based Image Segmentation Using K-Means Clustering and Normalised Cuts

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