He Junheng, Liu Shu, Di Hongwei. TLD target tracking algorithm based on dynamic capture[J]. Opto-Electronic Engineering, 2018, 45(8): 180030. doi: 10.12086/oee.2018.180030
Citation: He Junheng, Liu Shu, Di Hongwei. TLD target tracking algorithm based on dynamic capture[J]. Opto-Electronic Engineering, 2018, 45(8): 180030. doi: 10.12086/oee.2018.180030

TLD target tracking algorithm based on dynamic capture

    Fund Project: Supported by Guangdong Science and Technology Project (2013B010204055)
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  • In order to improve the ability of processing video image with high frame rate and meet the real-time requirement in higher resolution video tracking goal, DC-TLD target tracking algorithm, which is based on TLD algorithm and the theory of capturing sample dynamically, is proposed. Firstly, DC-TLD takes the target location of the previous frame as the predicted value of the current frame. Secondly, it calculates the threshold r of negative samples for ensuring that the number of negative samples is sufficient. Thirdly, it accesses the samples by index. The results of experiments show that DC-TLD is more robust and more efficient.
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  • [1] 程卫亮, 王向军, 万子敬, 等.压缩域目标跟踪算法在小型化DSP平台上的研究与实现[J].光电工程, 2017, 44(10): 972–982. doi: 10.3969/j.issn.1003-501X.2017.10.005

    CrossRef Google Scholar

    Cheng W L, Wang X J, Wan Z J, et al. Research and implementation of target tracking algorithm in compression domain on miniaturized DSP platform[J]. Opto-Electronic Engineering, 2017, 44(10): 972–982. doi: 10.3969/j.issn.1003-501X.2017.10.005

    CrossRef Google Scholar

    [2] Henriques J F, Rui C, Martins P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583–596. doi: 10.1109/TPAMI.2014.2345390

    CrossRef Google Scholar

    [3] Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 4293–4302.http://ieeexplore.ieee.org/document/7780834/

    Google Scholar

    [4] Nam H, Baek M, Han B. Modeling and propagating CNNs in a tree structure for visual tracking[EB/OL]. https://arxiv.org/abs/1608.07242v1, 2016.

    Google Scholar

    [5] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409–1422. doi: 10.1109/TPAMI.2011.239

    CrossRef Google Scholar

    [6] Hare S, Saffari A, Torr P H S. Struck: structured output tracking with kernels[C]//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2012: 263–270.http://ieeexplore.ieee.org/xpls/abs_all.jsp%3Farnumber%3D6126251

    Google Scholar

    [7] Brox T, Bruhn A, Papenberg N, et al. High accuracy optical flow estimation based on a theory for warping[C]//Proceedings of 2004 Computer Vision-ECCV. Berlin, Heidelberg: Springer, 2004: 25–36.http://link.springer.com/10.1007/978-3-540-24673-2_3

    Google Scholar

    [8] Kalal Z, Mikolajczyk K, Matas J. Forward-backward error: automatic detection of tracking failures[C]//Proceedings of 2010 20th International Conference on Pattern Recognition. Istanbul, Turkey: IEEE, 2010: 2756–2759.http://doi.ieeecomputersociety.org/10.1109/ICPR.2010.675

    Google Scholar

    [9] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai: IEEE, 2003.http://doi.ieeecomputersociety.org/10.1109/CVPR.2001.990517

    Google Scholar

    [10] Kalal Z, Matas J, Mikolajczyk K. P-N learning: bootstrapping binary classifiers by structural constraints[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE, 2014: 49–56.http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5540231

    Google Scholar

    [11] 江博. 基于Kalman的TLD目标跟踪算法研究[D]. 西安: 西安科技大学, 2013.

    Google Scholar

    Jiang B. Research on TLD algorithm based on Kalman filter[D]. Xi'an: Xi'an University of Science and Technology, 2013.http://cdmd.cnki.com.cn/Article/CDMD-10704-1014000315.htm

    Google Scholar

    [12] 王美健, 吴小俊.适于非线性机动目标跟踪的新IMM平滑算法[J].光电工程, 2016, 43(10): 18–24. doi: 10.3969/j.issn.1003-501X.2016.10.004

    CrossRef Google Scholar

    Wang M J, Wu X J. New IMM smoothing algorithm suitable for nonlinear maneuvering target tracking[J]. Opto-Electronic Engineering, 2016, 43(10): 18–24. doi: 10.3969/j.issn.1003-501X.2016.10.004

    CrossRef Google Scholar

    [13] Wu Y, Lim J, Yang M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834–1848. doi: 10.1109/TPAMI.2014.2388226

    CrossRef Google Scholar

  • Overview: The target tracking technology based on computer vision is widely used in civil and military fields, such as traffic monitoring, security monitoring, uav tracking and human-computer interaction. However, target tracking is hardly applied to the actual scene. The main reasons are as followed. To begin with, target may suffer from deformation, illumination variation and background clutter during the tracking, which will make the tracking system lose the target. What's more, target may be vague due to the fast motion. Last but not least, target may be blocked by something. In addition, many target tracking algorithms are too complicated to complete real-time tracking.

    The tracking-learning-detection algorithm is a new single-target long time tracking algorithm proposed by Zdenek Kalal. The tracking-learning-detection (TLD) algorithm is different with the algorithm which is based on the conventional tracking algorithm. The TLD algorithm combines the tracking algorithm and the detection algorithm to solve the problem about the shape change, fast moving and partial shade of the target during the tracking. At the same time, the target model and related parameters of tracking module and detection module are constantly updated through online learning module, which makes the tracking effect more stable, robust and reliable. However, TLD target tracking algorithm has high complexity and it is difficult to achieve the real-time effect of target tracking. Because the TLD algorithm has good robustness and is suitable for application to actual tracking process, this paper studies how to improve and optimize the TLD algorithm.

    By analyzing the characteristics of the TLD algorithm, this paper proposes DC-TLD target tracking algorithm based on dynamic capture region. Firstly, by changing the target location prediction method, the position of the previous frame is used to predict the position of the target in the current frame, which will reduce the detection time of the target sample. Secondly, the sample selection method is improved to obtain enough positive and negative samples in the small sample selection. Thirdly, the sample access method is improved by numbering the sample, and the access speed of the sample is improved by index access. The above measures successfully reduce the complexity of the TLD target tracking algorithm and improve the robustness of the algorithm, which make the algorithm closer to real-time requirements. However, when the tracking target is large, the tracking effect is similar as DC-TLD algorithm and TLD algorithm. And it is hard for DC-TLD to track multiple targets.

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