Zongda Liu, Liquan Dong, Yuejin Zhao, Lingqin Kong, Ming Liu. Adaptive Model Tracking Algorithm for Fast-Moving Targets in Video[J]. Acta Optica Sinica, 2021, 41(18): 1815001
- Acta Optica Sinica
- Vol. 41, Issue 18, 1815001 (2021)
Fig. 1. Application of correlation filtering algorithm in visual tracking
Fig. 2. Flow chart of the correlation filtering target tracking algorithm
Fig. 3. Tracking effect with a learning rate of 0.01
Fig. 4. Tracking effect with a learning rate of 0.05
Fig. 5. Response graph and maximum response value when tracking is good. (a) Response graph; (b) tracking effect graph
Fig. 6. Response graph and maximum response value when tracking is difficult. (a) Response graph; (b) tracking effect graph
Fig. 7. Response graph and APCE value when tracking is good. (a) Response graph; (b) tracking effect graph
Fig. 8. Response graph and APCE value when tracking is difficult. (a) Response graph; (b) tracking effect graph
Fig. 9. Tracking results of fast motion objects by different algorithms. (a) Fixed model; (b) adaptive model
Fig. 10. OPE curves of different algorithms. (a) Average precision; (b) success rate
Fig. 11. OPE curves of different algorithms in fast motion target sequences. (a) Average precision; (b) success rate
Fig. 12. OPE curves of different algorithms in the motion blur target sequence. (a) Average precision; (b) success rate
Fig. 13. OPE curves of different algorithms in the rapid deformation target sequences. (a) Average precision; (b) success rate
Fig. 14. Test results of our algorithm on the skier data set
Fig. 15. Verification result of the recheck mechanism. (a) Target is blurred; (b) target is deformed; (c) poor continuity; (d) dislocation tracking
Fig. 16. OPE curve of our algorithm on the self-made data set. (a) Average precision; (b) success rate
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Table 1. Performance analysis of different algorithms
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