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Incremental structured dictionary learning for video sensor-based object tracking.

Xue M, Yang H, Zheng S, Zhou Y, Yu Z - Sensors (Basel) (2014)

Bottom Line: To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed.As the dictionary evolves, the models are also trained to timely adapt the target appearance variation.We also illustrate its relay application in visual sensor networks.

View Article: PubMed Central - PubMed

Affiliation: Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. xue@sjtu.edu.cn.

ABSTRACT
To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

No MeSH data available.


Generation of proposed dictionary, which is composed of positive and negative template patches learned by IDSDL and trivial templates. The corresponding patches are cropped separately from the positive and negative samples around the target based on different sampling radii.
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f2-sensors-14-03130: Generation of proposed dictionary, which is composed of positive and negative template patches learned by IDSDL and trivial templates. The corresponding patches are cropped separately from the positive and negative samples around the target based on different sampling radii.

Mentions: Based on the assumption and definition described above, we present an incremental discriminative structured dictionary learning method. A structured dictionary is defined as , as shown in Figure 2, where is its element corresponding to the i-th patch. Furthermore, is defined to be constructed as:(4)Dti=[Tt,Nti,I,−I](5)Tt=[[(Pt1:N)1],[(Pt1:N)2],…,[(Pt1:N)M]](6)Pti=[bt1,bt2,…,btM],Nti=[dt1,dt2,…,dtM]where refers to the matrix composed of N columns separately containing the j-th column of a matrix , i = 1, 2, …, N. refers to the dictionary part learned by , and refers to the part by , where and share the same patch sampling scheme with that of the target candidate. The relationship between and Tt is shown in Figure 3. I ∈ ℝd2×d2 is an identity matrix used as a non-negativity constraints similar with the settings in [11,15].


Incremental structured dictionary learning for video sensor-based object tracking.

Xue M, Yang H, Zheng S, Zhou Y, Yu Z - Sensors (Basel) (2014)

Generation of proposed dictionary, which is composed of positive and negative template patches learned by IDSDL and trivial templates. The corresponding patches are cropped separately from the positive and negative samples around the target based on different sampling radii.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958219&req=5

f2-sensors-14-03130: Generation of proposed dictionary, which is composed of positive and negative template patches learned by IDSDL and trivial templates. The corresponding patches are cropped separately from the positive and negative samples around the target based on different sampling radii.
Mentions: Based on the assumption and definition described above, we present an incremental discriminative structured dictionary learning method. A structured dictionary is defined as , as shown in Figure 2, where is its element corresponding to the i-th patch. Furthermore, is defined to be constructed as:(4)Dti=[Tt,Nti,I,−I](5)Tt=[[(Pt1:N)1],[(Pt1:N)2],…,[(Pt1:N)M]](6)Pti=[bt1,bt2,…,btM],Nti=[dt1,dt2,…,dtM]where refers to the matrix composed of N columns separately containing the j-th column of a matrix , i = 1, 2, …, N. refers to the dictionary part learned by , and refers to the part by , where and share the same patch sampling scheme with that of the target candidate. The relationship between and Tt is shown in Figure 3. I ∈ ℝd2×d2 is an identity matrix used as a non-negativity constraints similar with the settings in [11,15].

Bottom Line: To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed.As the dictionary evolves, the models are also trained to timely adapt the target appearance variation.We also illustrate its relay application in visual sensor networks.

View Article: PubMed Central - PubMed

Affiliation: Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. xue@sjtu.edu.cn.

ABSTRACT
To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

No MeSH data available.