| Citation: | WANG Xin, HOU Zhiqiang, YU Wangsheng, et al. Robust visual tracking based on deep sparse learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(12): 2554-2563. doi: 10.13700/j.bh.1001-5965.2016.0788(in Chinese)
|
In visual tracking, the efficient and robust feature representation plays an important role in tracking performance in complicated environment. Therefore, a deep sparse neural network model which can extract more intrinsic and abstract features was proposed. Meanwhile, the complex and time-consuming pre-training process was avoided by using this model. During online tracking, the method of data augmentation was employed in the single positive sample to balance the quantities of positive and negative samples, which can improve the stability of the model. The local confidence maps were generated through dense sampling search to overcome the phenomenon of sampling particle drift. In order to improve the robustness of the model, several corresponding strategies of updating model parameters and searching area are proposed respectively. Extensive experimental results indicate the effectiveness and robustness of the proposed algorithm in challenging environment compared with state-of-the-art tracking algorithms. The problem of tracking drift is alleviated significantly and the tracking speed is fast.
| [1] |
SMEULDERS A W M, CHU D M, CUCCHIARA R, et al.Visual tracking:An experimental survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7):1442-1468. doi: 10.1109/TPAMI.2013.230
|
| [2] |
侯志强, 韩崇昭.视觉跟踪技术综述[J].自动化学报, 2006, 32(4):603-617.
HOU Z Q, HAN C Z.A survey of visual tracking[J].Acta Automatica Sinica, 2006, 32(4):603-617(in Chinese).
|
| [3] |
LI X, HU W M, SHEN C H, et al.A survey of appearance models in visual object tracking[J].ACM Transactions on Intelligent Systems and Technology, 2013, 4(4):Article 58.
|
| [4] |
ROSS D A, LIM J, LIN R S.Incremental learning for robust visual tracking[J].International Journal of Computer Vision, 2008, 77(1-3):125-141. doi: 10.1007/s11263-007-0075-7
|
| [5] |
ZHANG T Z, GHANEM B, LIU S, et al.Robust visual tracking via multi-task sparse learning[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2012:2042-2049.
|
| [6] |
JIA X, LU H, YANG M H.Visual tracking via adaptive structural local sparse appearance model[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2012:1822-1829.
|
| [7] |
ZHANG K H, ZHANG L, YANG M H.Real-time compressive tracking[C]//Proceedings of European Conference on Computer Vision.Heidelberg:Springer Verlag, 2012, 7574:864-877.
|
| [8] |
KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-learning-detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422. doi: 10.1109/TPAMI.2011.239
|
| [9] |
BABENKO B, YANG M H, BELONGIE S.Robust object tracking with online multiple instance learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8):1619-1632. doi: 10.1109/TPAMI.2010.226
|
| [10] |
SCHMIDHUBER J.Deep learning in neural networks:An overview[J].Neural Network, 2014, 61:85-117.
|
| [11] |
WANG N Y, YEUNG D.Learning a deep compact image representation for visual tracking[C]//Proceedings of Advances in Neural Information Processing Systems.Lake Tahoe:NIPS Press, 2013:809-817.
|
| [12] |
XU T Y, WU X J.Visual object tracking via deep neural network[C]//2015 IEEE 1st International Smart Cities Conference.Piscataway, NJ:IEEE Press, 2015:1-6.
|
| [13] |
ZHANG K H, LIU Q S, WU Y, et al.Robust visual tracking via convolutional networks[J].IEEE Transactions on Image Processing, 2015, 25(4):1779-1792.
|
| [14] |
GLOROT X, BENGIO Y.Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of International Conference on Artificial Intelligence and Statistics.Brookline, MA:Microtome Publishing, 2010, 9:249-256.
|
| [15] |
WANG F S.Particle filters for visual tracking[C]//Proceedings of International Conference on Advanced Research on Computer Science and Information Engineering. Heidelberg:Springer Verlag, 2011, 152:107-112.
|
| [16] |
GLOROT X, BORDES A, BENGIO Y.Deep sparse rectifier neural networks[C]//Proceedings of International Conference on Artificial Intelligence and Statistics.Brookline, MA:Microtome Publishing, 2011, 15:315-323.
|
| [17] |
HINTON G E, SALAKHUTDINOV R.Reducing the dimensionality of data with neural networks[J].Science, 2006, 313(5786):504-507. doi: 10.1126/science.1127647
|
| [18] |
ZHANG Y, ZHANG E H, CHEN W J.Deep neural network for halftone image classification based on sparse auto-encoder[J].Engineering Applications of Artificial Intelligence, 2016, 50(1):245-255.
|
| [19] |
EIGEN D, PUHRSCH C, FERGUS R.Depth map prediction from a single image using multi-scale deep network[C]//Proceedings of Advances in Neural Information Processing Systems.Montreal:Springer, 2014:2366-2374.
|
| [20] |
GAO C, CHEN F, YU J G, et al.Robust visual tracking using exemplar-based detectors[J].IEEE Transactions on Circuits & Systems for Video Technology, 2017, 27(2):300-312.
|
| [21] |
WU Y, LIM J, YANG M H.Online object tracking:A benchmark[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2013, 9:2411-2418.
|
| [22] |
SEVILLA-LARA L, LEARNED-MILLER E.Distribution fields for tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2012:1910-1917.
|
| [1] | BAI He, LI Yu, HAO Ming, XU Tingting. Graph neural network recommendation algorithm fused with dual-channel attention and time encoding[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2024.0795 |
| [2] | LU S Q，GUAN F X，LAI H T，et al. Two-stage underwater image enhancement method based on convolutional neural networks[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（1）：321-332 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.1003. |
| [3] | JU T，LIU S，WANG Z Q，et al. Task segmentation and parallel optimization of DNN model[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（9）：2739-2752 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0731. |
| [4] | BAI F C，YANG X X，DENG X L，et al. Station keeping control for aerostat in wind fields based on deep reinforcement learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（7）：2354-2366 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0629. |
| [5] | LIANG Zhen-feng, XIA Hai-ying, TAN Yu-mei, SONG Shu-xiang. Aerial Image Stitching Algorithm Based on Unsupervised Deep Learning[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0366 |
| [6] | HOU Z Q，CHEN M L，MA J Y，et al. Siamese network visual tracking algorithm based on second-order attention[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（3）：739-747 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0373. |
| [7] | MA S G，ZHANG Z X，PU L，et al. Real-time robust visual tracking based on spatial attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：419-432 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0329. |
| [8] | CHAI G Q，BO X S，LIU H J，et al. Self-supervised scene depth estimation for monocular images based on uncertainty[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（12）：3780-3787 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0943. |
| [9] | WANG Bo, HE Yang, DU Xiao-xin, ZHANG Jian-fei, XU Jing-ran, JIA Na. Prediction of Microbe-drug association based on graph attention stacked autoencoder[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0730 |
| [10] | LU G，ZHONG T X，GENG J. A Transformer based deep conditional video compression[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：442-448 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0374. |
| [11] | HOU Z Q，MA J Y，HAN R X，et al. A fast long-term visual tracking algorithm based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（8）：2391-2403 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0645. |
| [12] | ZHANG J L，YANG X X，DENG X L，et al. Altitude control of stratospheric aerostat based on deep reinforcement learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（8）：2062-2070 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0622. |
| [13] | JIANG Y，CHEN M Y，YUAN Q，et al. Departure flight delay prediction based on spatio-temporal graph convolutional networks[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（5）：1044-1052 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0415. |
| [14] | WANG Y G，YAO S Z，TAN H B. Residual SDE-Net for uncertainty estimates of deep neural networks[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（8）：1991-2000 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0604. |
| [15] | PU L，LI H L，HOU Z Q，et al. Siamese network tracking based on high level semantic embedding[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（4）：792-803 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0319. |
| [16] | SUN X T，CHENG W，CHEN W J，et al. A visual detection and grasping method based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（10）：2635-2644 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0130. |
| [17] | ZHU Mengyuan, CHEN Zhuo, LIU Pengfei, LYU Na. Fog computing-based federated intrusion detection algorithm for wireless sensor networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(10): 1943-1950. doi: 10.13700/j.bh.1001-5965.2021.0766 |
| [18] | SU Kaiqi, YAN Weiqing, XU Jindong. 3D object detection based on multi-path feature pyramid network for stereo images[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1487-1494. doi: 10.13700/j.bh.1001-5965.2021.0525 |
| [19] | CHAI Guoqiang, WANG Dawei, LU Bin, LI Zhu. Lightweight densely connected network based on attention mechanism for single-image deraining[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(11): 2186-2192. doi: 10.13700/j.bh.1001-5965.2021.0294 |
| [20] | LIU Hao, YANG Xiaoshan, XU Changsheng. Long-tail image captioning with dynamic semantic memory network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1399-1408. doi: 10.13700/j.bh.1001-5965.2021.0518 |
| 1. | 侯志强，马靖媛，韩若雪，马素刚，余旺盛，范九伦. 基于深度学习的快速长时视觉跟踪算法. 北京航空航天大学学报. 2024(08): 2391-2403 .  本站查看 | |
| 2. | 杨锐，张宝华，张艳月，吕晓琪，谷宇，王月明，刘新，任彦，李建军. 基于深度特征自适应融合的运动目标跟踪算法. 激光与光电子学进展. 2020(18): 287-294 .  |
Figures(9) / Tables(2)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
ZHENG Yun, WEI Fa, JIANG Xinget al. Application of Micro Input-Output Theory to Enterprises' Logistics System[J]. Journal of Beijing University of Aeronautics and Astronautics, 2001, 27(5): 608-610. (in Chinese)
DownLoad:
