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Acta Optica Sinica, Volume. 39, Issue 11, 1115002(2019)

Adaptive Feature Update Object-Tracking Algorithm in Complex Situations

Kuan Yin1, Junli Li1、*, Li Li1, and Chengxi Chu2
Author Affiliations
  • 1College of Computer Science, Sichuan Normal University, Chengdu, Sichuan 610101, China
  • 2Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (15)
    Equations (0)
    References (26)
    Cited By (4)
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    Figures & Tables(15)
    Framework of proposed algorithm

    Fig. 1. Framework of proposed algorithm

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    Target-feature visualization. (a) Original image; (b) HOG feature; (c) conv3-4 feature; (d) conv4-4 feature; (e) conv5-4 feature

    Fig. 2. Target-feature visualization. (a) Original image; (b) HOG feature; (c) conv3-4 feature; (d) conv4-4 feature; (e) conv5-4 feature

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    Schematic of feature fusion

    Fig. 3. Schematic of feature fusion

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    Update mechanism of temporal model

    Fig. 4. Update mechanism of temporal model

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    Flow chart of algorithm

    Fig. 5. Flow chart of algorithm

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    Qualitative results of 10 tracking algorithms for some video sequences

    Fig. 6. Qualitative results of 10 tracking algorithms for some video sequences

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    Tracking results of different feature experts

    Fig. 7. Tracking results of different feature experts

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    Tracking accuracy and success rate of algorithm on OTB-2013 and OTB-2015 databases. (a) Tracking accuracy on OTB-2013 database; (b) tracking success rate on OTB-2013 database; (c) tracking accuracy on OTB-2015 database; (d) tracking success rate on OTB-2015 database

    Fig. 8. Tracking accuracy and success rate of algorithm on OTB-2013 and OTB-2015 databases. (a) Tracking accuracy on OTB-2013 database; (b) tracking success rate on OTB-2013 database; (c) tracking accuracy on OTB-2015 database; (d) tracking success rate on OTB-2015 database

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    Tracking precision of 11 different attribute video sequences on OTB-2013 database. (a) Background clutter; (b) deformation; (c) fast motion; (d) in-plane rotation; (e) illumination variation; (f) low resolution; (g) motion blur; (h) occlusion; (i) out-of-plane rotation; (j) out of view; (k) scale variation

    Fig. 9. Tracking precision of 11 different attribute video sequences on OTB-2013 database. (a) Background clutter; (b) deformation; (c) fast motion; (d) in-plane rotation; (e) illumination variation; (f) low resolution; (g) motion blur; (h) occlusion; (i) out-of-plane rotation; (j) out of view; (k) scale variation

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    Tracking success rates of 11 different attribute video sequences on OTB-2013 database. (a) Background clutter; (b) deformation; (c) fast motion; (d) in-plane rotation; (e) illumination variation; (f) low resolution; (g) motion blur; (h) occlusion; (i) out-of-plane rotation; (j) out of view; (k) scale variation

    Fig. 10. Tracking success rates of 11 different attribute video sequences on OTB-2013 database. (a) Background clutter; (b) deformation; (c) fast motion; (d) in-plane rotation; (e) illumination variation; (f) low resolution; (g) motion blur; (h) occlusion; (i) out-of-plane rotation; (j) out of view; (k) scale variation

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    Tracking precision of 11 different attribute video sequences on OTB-2015 database. (a) Background clutter; (b) deformation; (c) fast motion; (d) in-plane rotation; (e) illumination variation; (f) low resolution; (g) motion blur; (h) occlusion; (i) out-of-plane rotation; (j) out of view; (k) scale variation

    Fig. 11. Tracking precision of 11 different attribute video sequences on OTB-2015 database. (a) Background clutter; (b) deformation; (c) fast motion; (d) in-plane rotation; (e) illumination variation; (f) low resolution; (g) motion blur; (h) occlusion; (i) out-of-plane rotation; (j) out of view; (k) scale variation

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    Tracking success rates of 11 different attribute video sequences on OTB-2015 database. (a) Background clutter; (b) deformation; (c) fast motion; (d) in-plane rotation; (e) illumination variation; (f) low resolution; (g) motion blur; (h) occlusion; (i) out-of-plane rotation; (j) out of view; (k) scale variation

    Fig. 12. Tracking success rates of 11 different attribute video sequences on OTB-2015 database. (a) Background clutter; (b) deformation; (c) fast motion; (d) in-plane rotation; (e) illumination variation; (f) low resolution; (g) motion blur; (h) occlusion; (i) out-of-plane rotation; (j) out of view; (k) scale variation

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    Tracking precision and success rate of algorithm under long-term tracking. (a) Tracking precision;(b) tracking success rate

    Fig. 13. Tracking precision and success rate of algorithm under long-term tracking. (a) Tracking precision;(b) tracking success rate

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    • Table 1. Frequency statistics of feature experts

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      Table 1. Frequency statistics of feature experts

      Expert12345678
      Frequency1854188077852534

    • Table 2. Tracking accuracy, success rate, and speed of algorithm on OTB-2013 and OTB2015 databases

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      Table 2. Tracking accuracy, success rate, and speed of algorithm on OTB-2013 and OTB2015 databases

      DatabaseParameterOursSTRCFMCCTCF2ECOSRDCFADNetStapleKCFLCT
      OTB-2013Precision0.9020.8890.8830.8910.8550.8380.7980.7820.7400.848
      Success rate0.8760.8450.8630.8090.8060.7890.7210.7380.6230.738
      OTB-2015Precision0.8710.8640.8600.8450.8360.7880.7720.7840.6960.762
      Success rate0.8290.8000.8180.7510.7720.7300.7000.6990.5260.629
      Average FPS3.928.04.21.554.88.012.397.6349.329.4
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    Kuan Yin, Junli Li, Li Li, Chengxi Chu. Adaptive Feature Update Object-Tracking Algorithm in Complex Situations[J]. Acta Optica Sinica, 2019, 39(11): 1115002

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    Paper Information

    Category: Machine Vision

    Received: May. 31, 2019

    Accepted: Jul. 15, 2019

    Published Online: Nov. 6, 2019

    The Author Email: Li Junli (li.junli@vip.163.com)

    DOI:10.3788/AOS201939.1115002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology