Kernel Correlation Filter (KCF) tracker has shown great potential on precision, robustness and efficiency. However, the candidate region used to train the correlation filter is fixed, so tracking is difficult when the target escapes from the search window due to fast motion. In this paper, an improved KCF is put forward for long-term tracking. At first, the moth-flame optimization (MFO) algorithm is introduced into tracking to search for lost target. Then, the candidate sample strategy of KCF tracking method is adjusted by MFO algorithm to make it has the capability of fast motion tracking. Finally, we use the conservative learning correlation filter to judge the moving state of the target, and combine the improved KCF tracker to form a unified tracking framework. The proposed algorithm is tested on a self-made dataset benchmark. Moreover, our method obtains scores for both the distance precision plot (0.891 and 0.842) and overlap success plots (0.631 and 0.601) on the OTB-2013 and OTB-2015 data sets, respectively. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods, especially in dealing with fast or uncertain motion.
KristanM., et al., A novel performance evaluation methodology for single-target trackers, IEEE Transactions on Pattern Analysis and Machine Intelligence38(11) (2016), 2137–2155.
2.
SmeuldersA.W.M., et al., Visual tracking: An experimental survey, IEEE Transactions on Pattern Analysis and Machine Intelligence36(7) (2013), 1442–1468.
3.
PosseggerH., MauthnerT. and BischofH., In defense of color-based model-free tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015).
4.
BolmeD., BeveridgeJ., DraperB., et al., Visual object tracking using adaptive correlation filters, in IEEE Conference on Computer Vision and Pattern Recognition (2010), 2544–2550.
5.
HenriquesJ., CaseiroR., MartinsP., et al., High-speed tracking with kernelized correlation filters, IEEE Transactions on Pattern Analysis and Machine Intelligence37(3) (2015), 583–596.
6.
LiuB., ZhuZ. and YangY., Convolutional neural networks based scale-adaptive kernelized correlation filter for robust visual object tracking, Security, Pattern Analysis, and Cybernetics (SPAC), 2017 International Conference on. IEEE (2017), 423–428.
7.
QianK., ZhouH., RongS., et al., Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter, Infrared Physics & Technology82 (2017), 18–27.
8.
ZhangL. and SuganthanP., Robust visual tracking via co-trained Kernelized correlation filters, Pattern Recognition69 (2017), 82–93.
9.
JeongS., KimG. and LeeS., Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters, Sensors17(3) (2017), 433.
10.
UzkentB. and SeoY., EnKCF: Ensemble of Kernelized Correlation FilZers for High-Speed Object Tracking, arXiv preprint arXiv: 1801.06729, (2018).
11.
GaoL., LiY. and NingJ., Improved kernelized correlation filter tracking by using spatial regularization, Journal of Visual Communication and Image Representation50, 74–82.
12.
LiD. and ChenW., Object tracking with convolutional neural networks and kernelized correlation filters, Control And Decision Conference (CCDC) 2017 29th Chinese. 1039–1044, (2017).
13.
ZhangB., LiZ., CaoX., et al., Output constraint transfer for kernelized correlation filter in tracking, IEEE Transactions on Systems, Man, and Cybernetics: Systems47(4) (2017), 693–703.
14.
DingG., ChenW., ZhaoS., et al., Real-Time Scalable Visual Tracking via Quadrangle Kernelized Correlation Filters, IEEE Transactions on Intelligent Transportation Systems19(1) (2018), 140–150.
15.
LiC., LiuX., SuX., et al., Robust kernelized correlation filter with scale adaption for real-time single object tracking, Journal of Real-Time Image Processing (2018), 1–14.
16.
ZhouT., ZhuM., ZengD., et al., Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion, Mathematical Problems in Engineering (2017), 2017.
17.
WangJ., LiuW., XingW., et al., Visual object tracking with multi-scale superpixels and color-feature guided kernelized correlation filters, Signal Processing: Image Communication63 (2018), 44–62.
18.
ZhangH., ZhangJ., WuQ., et al., Extended kernel correlation filter for abrupt motion tracking, KSII Transactions on Internet & Information Systems11(9) (2017).
19.
ZhangX., HuW., MaybankS., et al., Sequential particle swarm optimization for visual tracking, Computer Vision and Pattern Recognition, CVPR 2008. IEEE Conference on. IEEE, 1–8, (2008).
20.
KangK., BaeC., YeungH., et al., A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization, Applied Soft Computing66 (2018), 319–329.
21.
WangF., LinB. and LiX., An ant particle filter for visual tracking, Computer and Information Science (ICIS), 2017 IEEE/ACIS 16th International Conference on. IEEE: 417–422, (2017).
22.
NguyenH. and BhanuB., Real-time pedestrian tracking with bacterial foraging optimization, Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on. IEEE, 37–42, (2012).
23.
GaoM., ShenJ.,
YinL., et al., A novel visual tracking method using bat algorithm, Neurocomputing177 (2016), 612–619.
DanelljanM., HägerG., KhanF., et al., Accurate scale estimation for robust visual tracking, British Machine Vision Conference, Nottingham (2014), 1–5.
26.
ZhangK., ZhangL. and YangM., Fast compressive tracking, IEEE Transactions on Pattern Analysis & Machine Intelligence (1) (2014), 1–1.
27.
ZhangK., ZhangL., LiuQ., et al., Fast visual tracking via dense spatio-temporal context learning, European Conference on Computer Vision. Springer, Cham, (2014), 127–141.
28.
WangD., LuH. and YangM., Least soft-threshold squares tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013), 2371–2378.
29.
MuellerM., SmithN. and GhanemB., Context-aware correlation filter tracking, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2(3) (2017), 6.
30.
WuY., LimJ. and YangM.H., Online object tracking: A benchmark, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013), 2411–2418.
31.
WuY., LimJ. and YangM.H., Object tracking benchmark, IEEE Transactions on Pattern Analysis and Machine Intelligence37(9) (2015), 1834–1848.
32.
SimonyanK. and ZissermanA., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv: 1409.1556, (2014).
33.
MaC., HuangJ.B., YangX., et al., Hierarchical convolutional features for visual tracking, Proceedings of the IEEE International Conference on Computer Vision (2015), 3074–3082.
34.
DanelljanM., HagerG., Shahbaz KhanF., et al., Learning spatially regularized correlation filters for visual tracking, Proceedings of the IEEE International Conference on Computer Vision (2015), 4310–4318.
35.
BertinettoL., ValmadreJ., GolodetzS., et al., Staple: Complementary learners for real-time tracking, Proceedings of the IEEE conference on computer vision and pattern recognition (2016), 1401–1409.
36.
ZhangJ., MaS. and SclaroffS., MEEM: robust tracking via multiple experts using entropy minimization, European Conference on Computer Vision. Springer, Cham, 188–203, (2014).
37.
MaC., YangX., ZhangC., et al., Long-term correlation tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), 5388–5396.
38.
LiY. and ZhuJ., A scale adaptive kernel correlation filter tracker with feature integration, European Conference on Computer Vision. Springer, Cham, 254–265, (2014).
39.
WangQ., GaoJ., XingJ., et al., Dcfnet: Discriminant correlation filters network for visual tracking, arXiv preprint arXiv: 1704.04057, (2017).
