Object Tracking in Video Using the TLD and CMT Fusion Model
DOI:
https://doi.org/10.24203/ijcit.v10i5.151Keywords:
Object Tracking, Tracking-Learning-Detection (TLD), Clustering of Static-Adaptive Correspondences for Deformable Object Tracking (CMT)Abstract
Object tracking has been an attractive study topic in computer vision in recent years, thanks to the development of video monitoring systems. Tracking-Learning Detection (TLD), Compressive Tracking (CT), and Clustering of Static-Adaptive Correspondences for Deformable Object Tracking are some of the state-of-the-art methods for motion object tracking (CMT). We present a fusion model that combines TLD and CMT in this study. To restrict the calculation time of the CMT technique, the fusion TLD CMT model enhanced the TLD benefits of computation time and accuracy on t no deformable objects. The experimental results on the Vojir dataset for three techniques (TLD, CMT, and TLD CMT) demonstrated that our fusion proposal successfully trades off CMT accuracy for computing time.
References
Hare, Sam, et al. "Struck: Structured Output Tracking with Kernels." (2014).
Park, Eunae, et al. "Tracking-Learning-Detection Adopted Unsupervised Learning Algorithm." Knowledge and Systems Engineering (KSE), The Seventh International Conference on. IEEE (2015).
Li, Xi, et al. "A survey of appearance models in visual object tracking." ACM transactions on Intelligent Systems and Technology (TIST) 4.4 (2013): 58.
Erdem, Erkut, Séverine Dubuisson, and Isabelle Bloch. "Fragments based tracking with adaptive cue integration." Computer vision and image understanding 116.7 (2012): 827-841.
Zhang, Kaihua, Lei Zhang, and Ming-Hsuan Yang. "Real-time compressive tracking." Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 864-877.
Nebehay, Georg, and Roman Pflugfelder. "Clustering of Static-Adaptive Correspondences for Deformable Object Tracking." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
A.T. Vo, T.Q. Le, H.S. Tran, and T.H. Le, “A Fusion TLD and CMT Model for Motion Object Tracking,” The International Conference on Information, System and Convergence Applications, July 13-16 in Chiang Mai, Thailand, ISSN 2383-479X, 2016, vol.3 no.1, pp. 60-63.
Ciaparrone, Gioele, et al. "Deep learning in video multi-object tracking: A survey." Neurocomputing 381 (2020): 61-88.
Lee, Sang Gu. "A Study on Utilizing Smartphone for CMT Object Tracking Method Adapting Face Detection." The Journal of the Convergence on Culture Technology 7.1 (2021): 588-594.
Zhen, Xinxin, et al. "A visual object tracking algorithm based on improved TLD." Algorithms 13.1 (2020): 15.
Li, Zhiyong, et al. "Learning a dynamic feature fusion tracker for object tracking." IEEE Transactions on Intelligent Transportation Systems (2020).
Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas. "Tracking-Learning-Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 34, no 7, Page: 1409-1422, Jul. 2012.
T. Vojir, and J. Matas, "The enhanced flock of tracker" In RRIV 2014.
Guo, Jr-Hung, and Kuo-Lan Su. "Using Laser Range Finder and Multitarget Tracking-Learning-Detection Algorithm for Intelligent Mobile Robot." Sensors and Materials 27.8 (2015): 755-761.
Staniszewski, Michał, et al. "Recent Developments in Tracking Objects in a Video Sequence." Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2016. 427-436.
Tran, Hai, et al. "Burn Image Classification Using One-Class Support Vector Machine." Context-Aware Systems and Applications. Springer International Publishing, 2015. 233-242.
Li, Jiahe, Xu Gao, and Tingting Jiang. "Graph networks for multiple object tracking." Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2020.
Yu, Hongyang, et al. "Conditional GAN based individual and global motion fusion for multiple object tracking in UAV videos." Pattern Recognition Letters 131 (2020): 219-226.
Abbass, Mohammed Y., et al. "A survey on online learning for visual tracking." The Visual Computer 37 (2021):
Bouraya Jr, Sara, and Abdessamad Belangour. "Multi object tracking: a survey." Thirteenth International Conference on Digital Image Processing (ICDIP 2021). Vol. 11878. International Society for Optics and Photonics, 2021. 993-1014.
Dutta, Anjan, et al. "Vision tracking: A survey of the state-of-the-art." SN Computer Science 1.1 (2020): 1-19.
Karthik, Shyamgopal, Ameya Prabhu, and Vineet Gandhi. "Simple unsupervised multi-object tracking." arXiv preprint arXiv:2006.02609 (2020).
Liang, Siyuan, et al. "Efficient adversarial attacks for visual object tracking." European Conference on Computer Vision. Springer, Cham, 2020.
Qiu, Ji, et al. "Two motion models for improving video object tracking performance." Computer Vision and Image Understanding 195 (2020): 102951.
Jiang, Shaokui, et al. "Faster and Simpler Siamese Network for Single Object Tracking." arXiv preprint arXiv:2105.03049 (2021).
Ahmadyan, Adel, et al. "Objectron: A large scale dataset of object-centric videos in the wild with pose annotations." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
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Copyright (c) 2021 Hai Tran
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The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
