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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.


Overlap rate (OR) evaluation for nine video clips. The proposed algorithm is compared with seven state-of-the-art methods: Frag [4], IVT [3], VTD [16], L1T [11], MIL [9], TLD [10] and PLS [18].
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f10-sensors-14-03130: Overlap rate (OR) evaluation for nine video clips. The proposed algorithm is compared with seven state-of-the-art methods: Frag [4], IVT [3], VTD [16], L1T [11], MIL [9], TLD [10] and PLS [18].

Mentions: Figures 9 and 10 separately illustrate the center error and overlap rate figures for all the quantitatively evaluated sequences. Based on these figures, it can be seen that our proposed algorithm can obtain narrow ranges of fluctuations against the other algorithms (e.g., David Indoor and Jumping). Though the values of the proposed tracker are not the best all the time, they are lower in the center error and higher in the overlap rate than the other algorithms in most test frames. Thus, the proposed tracker provides comprehensively more favorable results in CEE and AOR averages than the other algorithms described in Table 1.


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

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

Overlap rate (OR) evaluation for nine video clips. The proposed algorithm is compared with seven state-of-the-art methods: Frag [4], IVT [3], VTD [16], L1T [11], MIL [9], TLD [10] and PLS [18].
© Copyright Policy
Related In: Results  -  Collection

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

f10-sensors-14-03130: Overlap rate (OR) evaluation for nine video clips. The proposed algorithm is compared with seven state-of-the-art methods: Frag [4], IVT [3], VTD [16], L1T [11], MIL [9], TLD [10] and PLS [18].
Mentions: Figures 9 and 10 separately illustrate the center error and overlap rate figures for all the quantitatively evaluated sequences. Based on these figures, it can be seen that our proposed algorithm can obtain narrow ranges of fluctuations against the other algorithms (e.g., David Indoor and Jumping). Though the values of the proposed tracker are not the best all the time, they are lower in the center error and higher in the overlap rate than the other algorithms in most test frames. Thus, the proposed tracker provides comprehensively more favorable results in CEE and AOR averages than the other algorithms described in Table 1.

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.