Abstract：Concerning the problem that Context-Ware Correlation Filter (CACF) algorithm based on artificial features has poor tracking performance under the situations of deformation, motion blur and low resolution and when the tracker encounters conditions like severe occlusion, it is easy to fall into local optimum and cause tracking failure, a new object tracking algorithm combining re-detection mechanism and Convolutional Regression Network (CRN) was proposed. In the training phase, the correlation filter was integrated into the deep neural network as a CRN layer, so that the network became a whole for end-to-end training. In the tracking phase, different network layers and their response values were merged through residual connections. At the same time, a re-detection mechanism was introduced to make the tracking algorithm recover from the potential tracking failure, and the re-detector would be activated when the response value was lower than the given threshold. Experimental results on the dataset OTB-2013 show that the proposed algorithm achieves 88.1% accuracy on 50 video sequences, which is 9.7 percentage points higher than the accuracy of original CACF algorithm, and has better results compared with original algorithm on video sequences with attributes like deformation and motion blur.
[1] YILMAZ A, JAVED O, SHAH M. Object tracking:a survey[J]. ACM Computing Surveys, 2006, 38(4):13-55. [2] CHENG Y. Mean shift, mode seeking clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8):790-799. [3] COMANICIU D, RAMESH V, MEER P. Real-time tracking of non-rigid objects using mean shift[C]//Proceedings of the 2003 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2003:2002-2142. [4] DOUCET A, de FREITAS N, GORDON N. Sequential Monte Carlo Methods in Practice[M]. Berlin:Springer, 2001:496-497. [5] RISTIC B, ARULAMPALAM S, GORDON N. Beyond the Kalman filter-particle filters for tracking applications[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 18(5):37-38. [6] DANELLJAN M, KHAN F S, FELSBERG M, et al. Adaptive color attributes for real-time visual tracking[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2014:1090-1097. [7] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of the 2010 Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2010:2544-2550. [8] HENRIQUES J F, RUI C, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of the 2012 European Conference on Computer Vision, LNCS 7575. Berlin:Springer, 2012:702-715. [9] HENRIQUES J F, RUI C, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583-596. [10] MUELLER M, SMITH N, GHANEM B. Context-aware correlation filter tracking[C]//Proceedings of the 2017 Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2017:1387-1395. [11] 管皓,薛向阳,安志勇.深度学习在视频目标跟踪中的应用进展与展望[J].自动化学报,2016,42(6):834-847. (GUAN H, XUE X Y, AN Z Y. Advances on application of deep learning for video-object tracking[J]. Acta Automatica Sinica, 2016, 42(6):834-847.) [12] 王鑫,侯志强,余旺盛,等.基于多层卷积特征融合的目标尺度自适应稳健跟踪[J].光学学报,2017,37(11):232-243. (WANG X, HOU Z Q, YU W S, et al. Target scale adaptive robust tracking based on fusion of multilayer convolutional features[J]. Acta Optica Sinica, 2017, 37(11):232-243.) [13] MA C, HUANG J-B, YANG X K, et al. Hierarchical convolutional features for visual tracking[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Washington, DC:IEEE Computer Society, 2015:3074-3082. [14] 熊昌镇,车满强,王润玲.基于稀疏卷积特征和相关滤波的实时视觉跟踪算法[J].计算机应用,2018,38(8):2175-2179. (XIONG C Z, CHE M Q, WANG R L. Real-time visual tracking algorithm based on correlation filters and sparse convolutional features[J]. Journal of Computer Applications, 2018, 38(8):2175-2179.) [15] QI Y, ZHANG S, QIN L, et al. Hedged deep tracking[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington,DC:IEEE Computer Society, 2016:4303-4311. [16] WU Y, LIM J, YANG M-H. Online object tracking:a benchmark[C]//Proceedings of the 2013 Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2013:2411-2418. [17] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional siamese networks for object tracking[C]//Proceedings of the 2016 European Conference on Computer Vision, LNCS 9914. Cham:Springer, 2016:850-865.
2019, Vol. 39 
