Multi-Domain Convolutional Neural Network Tracking Algorithm Based on Reconstructed Feature Combination

Dawei Yang; Xinfei Gong; Lin Mao; Rubo Zhang

doi:10.3788/LOP56.191501

[1] Vojir T, Noskova J, Matas J. Robust scale-adaptive mean-shift for tracking[J]. Pattern Recognition Letters, 49, 250-258(2014).

[2] Hare S, Golodetz S, Saffari A et al. Struck: structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2096-2109(2016).

[3] Ge B Y, Zuo X Z, Hu Y J. Long-term object tracking based on feature fusion[J]. Acta Optica Sinica, 38, 1115002(2018).

[4] Henriques J F, Caseiro R, Martins P et al. Exploiting the circulant structure of tracking-by-detection with kernels[M]. ∥Fitzgibbon A, Lazebnik S, Perona P, et al. Computer vision- ECCV 2012. Lecture notes in computer science. Berlin, Heidelberg: Springer, 7575, 702-715(2012).

[5] Zhou H Y, Yang Y, Wang S Y. Multiple object tracking algorithm based on kernel correlation filter[J]. Laser & Optoelectronics Progress, 55, 091502(2018).

[6] Danelljan M, Robinson A, Khan F S et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9909, 472-488(2016).

[7] Danelljan M, Bhat G, Khan F S et al. ECO: efficient convolution operators for tracking. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 6638-6646(2017).

[8] Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 4293-4302(2016).

[9] Wu Y, Lim J, Yang M H. Online object tracking: a benchmark. [C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 2411-2418(2013).

[10] Kristan M, Matas J, Leonardis A et al. The visual object tracking VOT2015 challenge results. [C]∥2015 IEEE International Conference on Computer Vision Workshop (ICCVW), December 7-13, 2015, Santiago, Chile. New York: IEEE, 564-586(2015).

[11] Lin S Z, Zheng Y, Lu X F et al. Adaptive tracking algorithm for aerial small targets based on multi-domain convolutional neural networks and autoregression model[J]. Acta Optica Sinica, 37, 1215006(2017).

[12] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[M]. ∥ Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8689, 818-833(2014).

[13] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[14] Hong S, You T, Kwak S et al. Online tracking by learning discriminative saliency map with convolutional neural network. [C]∥Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 6-11, 2015, Lille, France. Massachusetts: JMLR. org, 37, 597-606(2015).

[15] Nam H, Baek M, Han B. Modeling. -08-25)[2019-02-29]. https:∥arxiv., org/abs/1608, 07242(2016).

[16] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1409-1422(2012).