基于深度特征表达与学习的视觉跟踪算法研究

李寰宇; 毕笃彦; 杨源; 查宇飞; 覃兵; 张立朝

doi:10.11999/JEIT150031

基于深度特征表达与学习的视觉跟踪算法研究

doi: 10.11999/JEIT150031

1.
(空军工程大学航空航天工程学院西安 710038)
2.
(空军工程大学空管领航学院西安 710051)

基金项目:

国家自然科学基金(61202339, 61472443)和航空科学基金(20131996013)

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- 文章访问数: 1281
- HTML全文浏览量: 80
- PDF下载量: 1992
- 被引次数: 0
出版历程
- 收稿日期: 2015-01-06
- 修回日期: 2015-04-28
- 刊出日期: 2015-09-19

Research on Visual Tracking Algorithm Based on Deep Feature Expression and Learning

2.
(College of Aerospace Engineering, Air Force Engineering University, Xi&rsquo

摘要

摘要: 该文针对视觉跟踪中运动目标的鲁棒性跟踪问题，将深度学习引入视觉跟踪领域，提出一种基于多层卷积滤波特征的目标跟踪算法。该算法利用分层学习得到的主成分分析(PCA)特征向量，对原始图像进行多层卷积滤波，从而提取出图像更深层次的抽象表达，然后利用巴氏距离进行特征相似度匹配估计，进而结合粒子滤波算法实现目标跟踪。结果表明，这种多层卷积滤波提取到的特征能够更好地表达目标，所提跟踪算法对光照变化、遮挡、异面旋转、摄像机抖动都具有很好的不变性，对平面内旋转也具有一定的不变性，在具有此类特点的视频序列上表现出非常好的鲁棒性。
- 视觉跟踪 /
- 深度学习 /
- 主成分分析 /
- 卷积神经网络 /
- 粒子滤波
Abstract: For the robustness of visual object tracking, a new tracking algorithm based on multi-stage convolution filtering feature is proposed by introducing deep learning into visual tracking. The algorithm uses the Principal Component Analysis (PCA) eigenvectors obtained by stratified learning, to extract the deeper abstract expression of the original image by multi-stage convolutional filtering. Then the Bhattacharyya distance is used to evaluate the similarity among features. Finally, particle filter algorithm is combined to realize target tracking. The result shows that the feature obtained by multi-stage convolution filtering can express target better, the proposed algorithm has a better inflexibility to illumination, covering, rotation, and camera shake, and it exhibits very good robustness in video sequence with such characteristics.
- Visual tracking /
- Deep learning /
- Principal Component Analysis (PCA) /
- Convolutional neural network /
- Particle filter

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参考文献(23)

[1]	Li X, Hu W M, and Shen C H. A survey of appearance models in visual object tracking[J]. ACM Transactions on Intelligent Systems and Technology, 2013, 4(4): 5801-5848.
[2]	Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[3]	Clement F, Camille C, Laurent N, et al.. Learning hierarchical features for scene labeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1915-1929.
[4]	Alex K, Sutskever I, and Hinton G E. ImageNet classification with deep convolutional neural networks[C]. Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, 2012: 748-764.
[5]	Zhou S S, Chen Q C, and Wang X L. Convolutional deep networks for visual data classification[J]. Neural Processing Letters, 2013, 38(11): 17-27.
[6]	Abdel-Hamid O, Mohamed A R, Jiang H, et al.. Convolutional neural networks for speech recognition[J]. ACM Transactions on Audio, Speech, and Language Processing, 2014, 22(10): 1533-1545.
[7]	Chen X Y, Xiang S M, and Li C L. Vehicle detection in satellite images by hybrid deep convolutional neural networks [J]. IEEE Transactions on Geoscience and Remote Sensing Letters, 2014, 11(10): 1797-1801.
[8]	Evgeny A S, Denis M T, and Serge N A. Comparison of regularization methods for imagenet classification with deep convolutional neural networks[J]. AASRI Procedia, 2014, 6(8): 89-94.
[9]	Baldi P and Hornik K. Neural networks and principal component analysis: learning from examples without local minima[J]. Neural Networks, 1989, 2(1): 53-58.
[10]	Ross D, Lim Jong-woo, and Lin Ruei-Sung. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1): 125-141.
[11]	姚志均. 一种新的空间直方图相似性度量方法及其在目标跟踪中的应用[J]. 电子与信息学报, 2013, 35(7): 1644-1649.
[12]	Yao Z J. A new spatiogram similarity measure method and its application to object tracking[J]. Journal of Electronics Information Technology, 2013, 35(7): 1644-1649.
[13]	Zhang K H, Zhang L, and Yang M H. Real-time compressive tracking[C]. Proceedings of Europe Conference on Computer Vision, Florence, 2012: 864-877.
[14]	Sevilla-Lara L and Learned-Miller E. Distribution fields for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2011: 1910-1917.
[15]	Shaul O, Aharon B H, and Dan L. Locally orderless tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, 2012: 1940-1947.
[16]	Henriques J F, Caseiro R, and Martins P. High-speed tracking with kernelized correlation filters[J]. IEEE
[17]	Transactions on Pattern Analysis and Machine Intelligence, 2015, DOI: 10.1109/TPAMI.2014.2345390.
[18]	Hare S, Saffari A, and Torr P H S. Struck：structured output tracking with kernels[C]. Proceedings of IEEE International Conference on Computer Vision, Colorado, 2011: 263-270.
[19]	Thang Ba Dinh, Nam Vo, and Medioni G. Context tracker: exploring supporters and distracters in unconstrained environments[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2011: 1177-1184.
[20]	Liu Bai-yang, Huang Jun-zhou, and Yang Lin. Robust tracking using local sparse appearance model and K-selection [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2011: 1313-1320.
[21]	Junseok K and Kyoung M. Tracking by sampling trackers[C]. Proceedings of IEEE International Conference on Computer Vision, Colorado, 2011: 1195-1202.
[22]	Amit Adam, Ehud Rivlin, and Ilan Shimshoni. Robust fragments-based tracking using the integral histogram[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2006: 798-805.
[23]	Dorin Comaniciu, Visvanathan Ramesh, and Meer P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577.

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