Abstract
Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial applications. Several visual trackers have been presented in the last two decades. They target different tracking objectives. Object tracking from a real-time video is a challenging problem. Therefore, a robust tracker is required to consider many aspects of videos such as camera motion, occlusion, illumination effect, clutter, and similar appearance. In this paper, we propose an efficient object tracking algorithm that adaptively represents the object appearance using CNN-based features. A sparse measurement matrix is proposed to extract the compressed features for the appearance model without sacrificing the performance. We compress sample images of the foreground object and the background by the sparse matrix. When re-detection is needed, the tracking algorithm conducts an SVM classifier on the extracted features with online update in the compressed domain. A search strategy is proposed to reduce the computational burden in the detection step. Extensive simulations with a challenging video dataset demonstrate that the proposed tracking algorithm provides real-time tracking, while delivering substantially better tracking performance than those of the state-of-the-art techniques in terms of robustness, accuracy, and efficiency.
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Abbass MY, Kim H (2018) Blind image separation using pyramid technique. EURASIP J Image Video Proc 2018(1)
Abbass MY, Kim H, Abdelwahab SA, Haggag SS, El-Rabaie EM, Dessouky MI, Abd Fathi E, El-Samie (2019) Image deconvolution using homomorphic technique. Signal, Image and Video Processing 13(4):703–709
Abbass MY, Kwon K, Kim N et al (2020a) A survey on online learning for visual tracking. Vis Comput. https://doi.org/10.1007/s00371-020-01848-y
Abbass MY, Kwon K, Kim N et al (2020b) Efficient object tracking using hierarchical convolutional features model and correlation filters. Vis Comput. https://doi.org/10.1007/s00371-020-01833-5
Abbass MY, Kwon K, Alam MS et al (2020c) Image super resolution based on residual dense CNN and guided filters. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09824-3
Abbass MY, Kwon Ki-Chul, Kim Nam, Abdelwahab Safey A, Abd Fathi E, El-Samie Ashraf AM, Khalaf (2020d) Utilization of deep convolutional and handcrafted features for object tracking. Optik 218:164926
Achlioptas D (2003) Database-friendly random projections: Johnsonlindenstrauss with binary coins. J Comput Syst Sci 66(4):671–687
Alam MS, Kwon K-C, Alam MA, Abbass MY, Imtiaz SM, Kim N (2020) Trajectory-based air-writing recognition using deep neural network and depth sensor. Sensors 20:376
Babenko B, Yang M-H, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632
Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr Philip HS (2016) Staple: complementary learners for real-time tracking. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Candes E, Tao T (2006) Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans Inf Theory 52(12):5406–5425
Danelljan M, Häger G, Khan FS, Felsberg M (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575
Gundogdu E, Ozkan H, Alatan AA (2017) Extending correlation filter-based visual tracking by tree-structured ensemble and spatial windowing. IEEE Trans Image Process 26(11):5270–5283
Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: ECCV
Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596
Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422
Li P, Hastie T, Church K (2006) Very sparse random projections. In: International conference on knowledge discovery and data mining, pp 287–296
Li H, Shen C, Shi Q (2011) Real-time visual tracking using compressive sensing. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1305–1312
Li Y, Zhang Y, Xu Y, Wang J, Miao Z (2016) Robust scale adaptive kernel correlation filter tracker with hierarchical convolutional features. IEEE Signal Process Lett 23(8):1136–1140
Li X, Zhao H, Zhang L (2017) Pedestrian counting system based on multiple object detection and tracking. In: Liu D, Xie S, Li Y, Zhao D, El-Alfy ES (eds) Neural information processing. ICONIP 2017. Lecture notes in computer science, vol 10636, Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_9.
Liu L, Fieguth P (2012) Texture classification from random features. IEEE Trans Pattern Anal Mach Intell 34(3):574–586
Lukežič A, Vojíř T, Čehovin Z (2018) Discriminative correlation filter tracker with channel and spatial reliability. Int J Comput Vis. https://doi.org/10.1007/s11263-017-1061-3
Ma C, Huang JB, Yang X, Yang MH (2015a) Long-term correlation tracking. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition
Ma C, Huang J-B, Yang X, Yang M-H (2015b) Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition
Ma C, Huang J-B, Yang X, Yang M-H (2019) Robust visual tracking via hierarchical convolutional features. IEEE Trans Pattern Anal Mach Intell 41(11):2709–2723
Ross D, Lim J, Lin R, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1):125–141
Wang X, Chen D, Yang T, Hu B, Zhang J (2016) Action recognition based on object tracking and dense trajectories. In: IEEE international conference on automatica (ICA-ACCA). https://doi.org/10.1109/ICA-ACCA.2016.7778391.
Wright J, Yang A, Ganesh A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227
Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: Proceedings of European conference on computer vision, pp 864– 877
Zhang K, Zhang L, Yang M-H (2014) Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015
Zhang J, Ma S, Sclaroff S (2014b) MEEM: robust tracking via multiple experts using entropy minimization. In: Proceedings of the European conference on computer vision
Zhang B, Li Z, Cao X, Ye Q, Chen C, Shen L, Jill R et al (2017) Output constraint transfer for kernelized correlation filter in tracking. IEEE Trans Syst Man Cybern Syst 47(4):693–703
Acknowledgements
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-0-01462) and by the Korea Government, under the ITRC (Information Technology Research Center) support program (IITP-2020-2015-0-00448, IITP-2020-0-01846) supervised by the IITP (Institute for Information & communications Technology Promotion).
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Abbass, M.Y., Kwon, KC., Kim, N. et al. Visual tracking using convolutional features with sparse coding. Artif Intell Rev 54, 3349–3360 (2021). https://doi.org/10.1007/s10462-020-09905-7
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DOI: https://doi.org/10.1007/s10462-020-09905-7