An Easily Initialized Visual Tracking Algorithm Based on Similar Structure for Convolutional Neural Network
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摘要: 该文针对视觉跟踪中运动目标的鲁棒性跟踪问题,基于主成分分析(PCA)和卷积神经网络(CNN),提出一种易于初始化的类CNN提取深度特征的视觉跟踪算法。该算法首先利用仿射变换对原始图像进行处理,然后对归一化尺寸的图像进行分层PCA学习,将学习得到的PCA特征向量作为CNN结构中的各阶滤波器,完成特征提取网络的初始化,再利用特征提取网络获取目标的深层次表达。最后结合粒子滤波,利用一个简单的逻辑回归分类器通过分类估计实现目标跟踪。结果表明,利用这种易于初始化的CNN提取到的深度特征能够有效地区分目标和背景,具有很好的可区分性,提出的视觉跟踪算法对光照变化、尺度变化、遮挡、旋转和摄像机抖动等都具有良好的适应性,在许多视频序列上表现出了较好的鲁棒性和准确性。Abstract: On the issues about the robustness in visual object tracking, based on Principal Component Analysis (PCA) and Convolutional Neural Network (CNN), a novel visual tracking algorithm with deep feature, which is acquired from a easily initialized CNN structure, is proposed. First, the original image is processed by affine transformation. Next, layered PCA learning is used to process the normalized size image, the eigenvectors learned by PCA are used to be the filters of a CNN structure to realize initialization. Then, the deep expression of the object is extracted by this CNN structure. Finally, combining particle filter algorithm, a simple logistic regression classifier is used to realize target tracking. The result shows that the deep feature acquired from the easily initialized CNN structure has a better expressivity, it can distinguish the object and background effectively. The proposed algorithm has a better inflexibility to illumination, occlusion, rotation and camera shake, and it exhibits a good robustness and accuracy in many video sequences.
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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. 侯志强, 黄安奇, 余旺盛, 等. 基于局部分块和模型更新的视觉跟踪算法[J]. 电子与信息学报, 2015, 37(6): 1357-1364. doi: 10.11999/JEIT141134. HOU Zhiqiang, HUANG Anqi, YU Wangsheng, et al. Visual object tracking method based on local patch model and model update[J]. Journal of Electronics Information Technology, 2015, 37(6): 1357-1364. doi: 10.11999/ JEIT141134. 李寰宇, 毕笃彦, 杨源, 等. 基于深度特征表达与学习的视觉跟踪算法研究[J]. 电子与信息学报, 2015, 37(9): 2033-2039. doi: 10.11999/JEIT150031. LI Huanyu, BI Duyan, YANG Yuan, et al. Research on visual tracking algorithm based on deep feature expression and learning[J]. Journal of Electronics Information Technology, 2015, 37(9): 2033-2039. doi: 10.11999/JEIT150031. HINTON G E and SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. SUN Y, WANG X, and TANG X. Deep learning face representation from predicting 10,000 classes[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 1891-1898. 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. OUYANG W, CHU X, and WANG X. Multi-source deep learning for human pose estimation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 2337-2344. 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. 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. WANG N Y and YEUNG D Y. Learning a deep compact image representation for visual tracking[C]. Advances in Neural Information Processing Systems, Lake Tahoe, 2013: 125-137. LI H X, LI Y, and FATIH P. Deep track: learning discriminative feature representations by convolutional neural networks for visual tracking[C]. Proceedings of the British Machine Vision Conference, Nottingham, 2014: 110-119. 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. P?EREZ P, HUE C, and VERMAAK J. Color-based probabilistic tracking[C]. European Conference on Computer Vision, Copenhagen, 2002: 661-675. ZHANG K H, ZHANG L, and YANG M H. Real-time compressive tracking[C]. European Conference on Computer Vision, Florence, 2012: 864-877. SEVILLA-LARA L and LEARNED-MILLER E. Distribution fields for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2011: 1910-1917. ADAM A, RIVLIN E, and SHIMSHONI I. Robust fragments-based tracking using the integral histogram[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2006: 798-805. ROSS D, LIM Jongwoo, and LIN Rueisung. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1): 125-141. COMANICIU D, RAMESH V, and MEER P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577 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. LIU Baiyang, HUANG Junzhou, 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. 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. JUNSEOK K and KYOUNG M. Tracking by sampling trackers[C]. Proceedings of IEEE International Conference on Computer Vision, Colorado, 2011: 1195-1202. EVERINGHAM M, VAN GODL L, WILLIAMS C, et al. The pascal Visual Object Classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.
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