Adaptive Strategy Fusion Target Tracking Based on Multi-layer Convolutional Features

Yanjing SUN; Yunkai SHI; Xiao YUN; Xuran ZHU; Sainan WANG

doi:10.11999/JEIT180971

Volume 41 Issue 10

Oct. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(10): 2464-2470

Yanjing SUN, Yunkai SHI, Xiao YUN, Xuran ZHU, Sainan WANG. Adaptive Strategy Fusion Target Tracking Based on Multi-layer Convolutional Features[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2464-2470. doi: 10.11999/JEIT180971

Citation:

Yanjing SUN, Yunkai SHI, Xiao YUN, Xuran ZHU, Sainan WANG. Adaptive Strategy Fusion Target Tracking Based on Multi-layer Convolutional Features[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2464-2470. doi: 10.11999/JEIT180971

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Adaptive Strategy Fusion Target Tracking Based on Multi-layer Convolutional Features

doi: 10.11999/JEIT180971

School of Information and Control Engineering, China University of Mining Technology, Xuzhou 221116, China

Funds: The Natural Science Foundation of Jiangsu Province (BK20180640, BK20150204), The Research Development Programme of Jiangsu Province (BE2015040), The State Key Research Development Program (2016YFC0801403), The National Natural Science Foundation of China (51504214, 51504255, 51734009, 61771417)

Received Date: 2018-10-17
Rev Recd Date: 2019-02-26

Available Online: 2019-03-16

Publish Date: 2019-10-01

Abstract

Abstract

To solve the problems of low robustness and tracking accuracy in target tracking when interference factors occur such as target fast motion and occlusion in complex video scenes, an Adaptive Strategy Fusion Target Tracking algorithm (ASFTT) is proposed based on multi-layer convolutional features. Firstly, the multi-layer convolutional features of frame images in Convolutional Neural Network(CNN) are extracted, which avoids the defect that the target information of the network is not comprehensive enough, so as to increase the generalization ability of the algorithm. Secondly, in order to improve the tracking accuracy of the algorithm, the multi-layer features are performed to calculate the correlation responses, which improves the tracking accuracy. Finally, the target position strategy in all responses are dynamically merged to locate the target through the adaptive strategy fusion algorithm in this paper. It comprehensively considers the historical strategy information and current strategy information of each responsive tracker to ensure the robustness. Experiments performed on the OTB2013 evaluation benchmark show that that the performance of the proposed algorithm are better than those of the other six state-of-the-art methods.
- Target tracking,
- Convolutional Neural Network(CNN),
- Correlation response,
- Strategy fusion

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References(23)

References

侯志强, 张浪, 余旺盛, 等. 基于快速傅里叶变换的局部分块视觉跟踪算法[J]. 电子与信息学报, 2015, 37(10): 2397–2404. doi: 10.11999/JEIT150183

HOU Zhiqiang, ZHANG Lang, YU Wangsheng, et al. Local patch tracking algorithm based on fast fourier transform[J]. Journal of Electronics &Information Technology, 2015, 37(10): 2397–2404. doi: 10.11999/JEIT150183

HUANG C, LUCEY S, and RAMANAN D. Learning policies for adaptive tracking with deep feature cascades[C]. Proceedings of IEEE International Conference on Computer Vision, Venice, Italy, 2017: 105–114.

KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[C]. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, 2012: 1097–1105.

WANG Linzhao, WANG Lijun, LU Huchuan, et al. Saliency detection with recurrent fully convolutional networks[C]. Proceedings of the 14th Computer Vision European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 825–841.

LONG J, SHELHAMER E, and DARRELL T. Fully convolutional networks for semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 3431–3440.

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. Cham: Springer, 2016: 472–488.

WANG Naiyan and YEUNG D Y. Learning a deep compact image representation for visual tracking[C]. Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, 2013: 809–817.

BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional siamese networks for object tracking[C]. Proceedings of the Computer Vision – ECCV 2016 Workshops, Amsterdam, The Netherlands, 2016: 850–865.

DALAL N and TRIGGS B. Histograms of oriented gradients for human detection[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005: 886–893.

TIAN Gang, HU Ruimin, WANG Zhongyuan, et al. Improved object tracking algorithm based on new hsv color probability model[M]. YU Wen, HE Haibo, ZHANG Nian. Advances in Neural Networks – ISNN 2009. Berlin Heidelberg, Springer, 2009: 1145–1151.

孙航, 李晶, 杜博, 等. 基于多阶段学习的相关滤波目标跟踪[J]. 电子学报, 2017, 45(10): 2337–2342. doi: 10.3969/j.issn.0372-2112.2017.10.004

SUN Hang, LI Jing, DU Bo, et al. Correlation filtering target tracking based on online multi-lifespan learning[J]. Acta Electronica Sinica, 2017, 45(10): 2337–2342. doi: 10.3969/j.issn.0372-2112.2017.10.004

DANELLJAN M, HÄGER G, KHAN F S, et al. Accurate scale estimation for robust visual tracking[C]. Proceedings of British Machine Vision Conference, Nottingham, UK, 2014: 65.1–65.11.

HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]. Proceedings of the 12th European Conference on Computer Vision, Florence, Italy, 2012: 702–715.

HENRIQUES J F, CASEIRO R, 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. doi: 10.1109/tpami.2014.2345390

HELD D, THRUN S, and SAVARESE S. Learning to track at 100 FPS with deep regression networks[C]. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 749–765.

TAO Ran, GAVVES E, and SMEULDERS A W M. Siamese instance search for tracking[C]. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1420–1429.

ZHANG Hainan, SUN Yanjing, LI Song, et al. Long-term tracking based on multi-feature adaptive fusion for video target[J]. IEICE Transactions on Information and Systems, 2018.

CHAUDHURI K, FREUND Y, and HSU D. A parameter-free hedging algorithm[C]. Proceedings of the 22nd International Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2009: 297–305.

WU Yi, LIM J, and YANG M H. Online object tracking: a benchmark[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2411–2418.

MUELLER M, SMITH N, and GHANEM B. Context-aware correlation filter tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 1387–1395.

DANELLJAN M, HÄGER G, KHAN F S, et al. Discriminative scale space tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1561–1575. doi: 10.1109/TPAMI.2016.2609928

BERTINETTO L, VALMADRE J, GOLODETZ S, et al. Staple: complementary learners for real-time tracking[C]. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1401–1409.

ZHANG Kaihua, LIU Qingshan, WU Yi, et al. Robust visual tracking via convolutional networks without training[J]. IEEE Transactions on Image Processing, 2016, 25(4): 1779–1792.

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