Journal of Optoelectronics · Laser, Volume. 35, Issue 12, 1259(2024)

Dual mode fusion object tracking network based on hybrid attention

QUAN Jiarui, HE Lesheng, YAN Kaixiang, YIN Heng, YU Shengtao, and LIAO Wei
Author Affiliations
  • College of Information, Yunnan University, Kunming, Yunnan 650000, China
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    References(23)

    [1] [1] KRISTAN M, MATAS J, LEONARDIS A, et al. The visual object tracking VOT2015 challenge results[C]//IEEE International Conference on Computer Vision (ICCV), December 11-18, 2015, Santiago, CHILE. New York: IEEE, 2015: 564-586.

    [2] [2] YANG L, KONG C, CHANG X, et al. Correlation filters with adaptive convolution response fusion for object tracking[J]. Knowledge-Based Systems, 2021, 228(C): 107314.

    [3] [3] WANG N, ZHOU W, WANG J, et al. Transformer meets tracker: exploiting temporal context for robust visual tracking[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021, Electr Network. New York: IEEE, 2021: 1571-1580.

    [5] [5] LI S, ZHANG X, XIONG J, et al. Learning spatial self-attention information for visual tracking[J]. IET Image Processing, 2022, 16: 49-60.

    [7] [7] KIM H U, LEE D Y, SIM J Y, et al. SOWP: Spatially ordered and weighted patch descriptor for visual tracking[C]//IEEE International Conference on Computer Vision, December 11-18, 2015, Santiago, CHILE. New York: IEEE, 2015: 3011-3019.

    [8] [8] LI C, WU X, ZHAO N, et al. Fusing two-stream convolutional neural networks for RGB-T object tracking[J]. Neurocomputing, 2018, 281: 78-85.

    [10] [10] LI C, ZHAO N, LU Y, et al. Weighted sparse representation regularized graph learning for RGB-T object tracking[C]//25th ACM International Conference on Multimedia (MM), October 23-27, 2017, Mountain View, CA. New York: IEEE, 2017: 1856-1864.

    [11] [11] LI C, CHENG H, HU S, et al. Learning collaborative sparse representation for grayscale-thermal tracking[J]. IEEE Transactions on Image Processing, 2016, 25(12): 5743-5756.

    [12] [12] SCHNELLE S R, CHAN A L. Enhanced target tracking through infrared-visible image fusion[C]//14th International Conference on Information Fusion, July 5-8, 2011, Chicago, USA. New York: IEEE, 2011: 1-8.

    [13] [13] ZHAI S, SHAO P, LIANG X, et al. Fast RGB-T tracking via cross-modal correlation filters[J]. Neurocomputing, 2019, 334: 172-81.

    [14] [14] ZHU Y B, LI C L, TANG J, et al. Quality-aware feature aggregation network for robust RGBT tracking[J]. IEEE Transactions on Intelligent Vehicles, 2021, 6(1): 121-30.

    [15] [15] ZHANG X M, ZHANG X H, DU X D, et al. Learning multi-domain convolutional network for RGB-T visual tracking[C]//11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), October 13-15, 2018, Beijing, China. New York: IEEE, 2018: 1-6.

    [16] [16] LI C, LU A, ZHENG A, et al. Multi-adapter RGBT tracking[C]//IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, South Korea. New York: IEEE, 2019: 2262-2270.

    [17] [17] ZHU Y, LI C, TANG J, et al. RGBT tracking by trident fusion network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(2): 579-592.

    [18] [18] XIAO Y, YANG M, LI C, et al. Attribute-based progressive fusion network for RGBT tracking[C]//36th AAAI Conference on Artificial Intelligence /34th Conference on Innovative Applications of Artificial Intelligence /12th Symposium on Educational Advances in Artificial Intelligence, February 22-March 1, 2022, Electr Network. Menlo Park, CA: AAAI, 2022: 2831-2838.

    [19] [19] CHANDRAKANTH V, MURTHY V, CHANNAPPAYYA S S. Siamese cross-domain tracker design for seamless tracking of targets in RGB and thermal videos[J]. IEEE Transactions on Artificial Intelligence, 2022, 4(1): 161-172.

    [20] [20] ZHANG X, YE P, PENG S, et al. SiamFT: An RGB-infrared fusion tracking method via fully convolutional siamese networks[J]. IEEE Access, 2019, 7: 122122-122133.

    [21] [21] ZHANG P, ZHAO J, BO C, et al. Jointly modeling motion and appearance cues for robust RGB-T tracking[J]. IEEE Transactions on Image Processing, 2021, 30: 3335-3347.

    [22] [22] DANELLJAN M, BHAT G, KHAN F S, et al. ECO: Efficient convolution operators for tracking[C]//30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 2017: 6931-6939.

    [23] [23] ZHU Y, LI C, LUO B, et al. Dense feature aggregation and pruning for RGBT tracking[C]//27th ACM International Conference on Multimedia (MM), October 21-25, 2019, Nice, France. New York: ACM, 2019: 465-472.

    [24] [24] PU S, SONG Y, MA C, et al. Deep attentive tracking via reciprocative learning[C]//Advances in Neural Information Processing Systems, December 3-8, 2018, Montreal, Canada. Red Hook: Curran Associates Inc., 2018, 31: 1935-1945.

    [25] [25] JUNG I, SON J, BAEK M, et al. Real-time MDNet[C]//15th European Conference on Computer Vision (ECCV), September 8-14, 2018, Munich, Germany, Cham: Springer, 2018: 89-104.

    [26] [26] LI C, LIANG X, LU Y, et al. RGB-T object tracking: Benchmark and baseline[J]. Pattern Recognition, 2019, 96: 106977.

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    QUAN Jiarui, HE Lesheng, YAN Kaixiang, YIN Heng, YU Shengtao, LIAO Wei. Dual mode fusion object tracking network based on hybrid attention[J]. Journal of Optoelectronics · Laser, 2024, 35(12): 1259

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    Paper Information

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    Received: Mar. 29, 2023

    Accepted: Dec. 31, 2024

    Published Online: Dec. 31, 2024

    The Author Email:

    DOI:10.16136/j.joel.2024.12.0143

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