基于自适应深度稀疏网络的在线跟踪算法

侯志强; 王鑫; 余旺盛; 戴铂; 金泽芬芬

doi:10.11999/JEIT160762

基于自适应深度稀疏网络的在线跟踪算法

doi: 10.11999/JEIT160762

基金项目:

国家自然科学基金(61473309)；陕西省自然科学基础研究计划项目(2015JM6269, 2016JM6050)

计量
- 文章访问数: 1419
- HTML全文浏览量: 136
- PDF下载量: 298
- 被引次数: 0
出版历程
- 收稿日期: 2016-07-20
- 修回日期: 2016-12-16
- 刊出日期: 2017-05-19

Online Visual Tracking via Adaptive Deep Sparse Neural Network

Funds:

The National Natural Science Foundation of China (61473309), The Project Supported by Natural Science Basic Research Plan in Shaanxi Province (2015JM6269, 2016JM6050)

摘要

摘要: 视觉跟踪中，高效鲁棒的特征表达是解决复杂环境下跟踪漂移问题的关键。该文针对深层网络预训练复杂费时及单网络跟踪易漂移的问题，在粒子滤波框架下，提出一种基于自适应深度稀疏网络的在线跟踪算法。该算法利用ReLU激活函数，针对不同类型目标构建了一种具有自适应选择性的深度稀疏网络结构，仅通过有限标签样本的在线训练，就可得到鲁棒的跟踪网络。实验数据表明：与当前主流的跟踪算法相比，该算法的平均跟踪成功率和精度均为最好，且与同样基于深度学习的DLT算法相比分别提高了20.64%和17.72%。在光照变化、相似背景等复杂环境下，该算法表现出了良好的鲁棒性，能够有效地解决跟踪漂移问题。
- 视觉跟踪 /
- 在线训练 /
- 深度学习 /
- 自适应深度稀疏网络
Abstract: In visual tracking, the efficient and robust feature representation is the key factor to solve the problem of tracking drift in complex environments. Therefore, to solve the problems of the complex and time-consuming of the pre-training process of deep neural network and the drift of the single network tracking, an online tracking method based on an adaptive deep sparse network is proposed under the tracking structure of particle filter. A deep sparse neural network architecture, which can be adaptively selected according to different types of targets, is constructed with the implementation of the Rectifier Linear Unit (ReLU) activation function. The robustness of deep tracking network can be easily achieved only through the online training of limited labeled samples. The results of experiments show that, compared with the state-of-the-art tracking algorithm, the average success ratio and precision of the proposed algorithm are both the highest, and they are raised by 20.64% and 17.72% respectively contrasted with the Deep Learning Tracker (DLT) algorithm based on deep learning. The proposed method can solve the problems of tracking drift efficiently, and shows better robustness, especially for the complex environment such as illumination changes, background clutter and so on.
- Visual tracking /
- Online training /
- Deep learning /
- Adaptive deep sparse network

HTML全文

参考文献(26)

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.

WANG Naiyan, SHI Jianping, YEUNG Dityan, et al. Understanding and diagnosing visual tracking systems[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3101-3109. doi: 1109/ICCV.2015.355.

ROSS D A, LIM J, LIN R S, et al. 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.

BABENKO B, YANG M, and 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.

KALAL Z, MIKOLAJCZYK K, and 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.

ZHANG Kaihua, ZHANG Lei, and YANG Minghsuan. Real-time compressive tracking[C]. European Conference on Computer Vision, Florence, Italy, 2012: 864-877.

WU Yi, LIM Jongwoo, and YANG Minghsuan. Online object tracking: A benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013, 9(4): 2411-2418. doi: 10.1109/CVPR.2013.312.

MA Chao, HUANG Jiabin, YANG Xiaokang, et al. Hierarchical convolutional features for visual tracking[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3074-3082.

NAJAFABADI M M, VILLANUSTRE F, KHOSHGOFTAAR T M, et al. Deep learning applications and challenges in big data analytics[J]. Journal of Big Data, 2015, 2(1): 1-21. doi: 10.1186/s40537-014-0007-7.

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.

JIN J, DUNDAR A, BATES J, et al. Tracking with deep neural networks[C]. Annual Conference on Information Sciences and Systems, Baltimore, MD, USA, 2013: 213-217.

WANG Naiyan and YEUNG Dityan. Learning a deep compact image representation for visual tracking[C]. Advances in Neural Information Processing Systems, South Lake Tahoe, Nevada, USA, 2013: 809-817.

RUSSAKOVSKY O, DENG J, SU H, et al. Imagenet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252. doi: 10.1007/ s11263-015-0816-y.

侯志强, 戴铂, 胡丹, 等. 基于感知深度神经网络的视觉跟踪[J]. 电子与信息学报, 2016, 38(7): 1616-1623. doi: 10.11999/ JEIT151449.

HOU Zhiqiang, DAI Bo, HU Dan, et al. Robust visual tracking via perceptive deep neural network[J]. Journal of Electronics Information Technology, 2016, 38(7): 1616-1623. doi: 10.11999/JEIT151449.

GLOROT X and BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[C]. International Conference on Artificial Intelligence and Statistics, Sardinia, Italy, 2010: 249-256.

GLOROT X, BORDES A, and BENGIO Y. Deep sparse rectifier neural networks[C]. International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 2011: 315-323.

TOTH L. Phone recognition with deep sparse rectifier neural networks[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Vancounver, BC, Canada, 2013: 6985-6989. doi: 10.1109/ICASSP.2013.6639016.

VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: Iearning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(6): 3371-3408.

HE K, ZHANG X, REN S, et al. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1026-1034.

WU Y W, ZHAO H H, and ZAHNG L Q. Image denoising with retified linear units[J]. Lecture Notes in Computer Science, 2014, 8836: 142-149. doi: 10.1007/978-3-319- 12643-2_18.

LI X, HU W, SHEN C, et al. A survey of appearance models in visual object tracking[J]. ACM Transactions on Intelligent Systems and Technology, 2013, 4(4): 1-48. doi: 10.1145/ 2508037.2508039.

WANG F S. Particle filters for visual tracking[C]. International Conference on Advanced Research on Computer Science and Information Engineering, Zhengzhou, China, 2011: 107-112.

GRABNER H, GRABNER M, and BISCHOF H. Real-time tracking via on-line boosting[C]. British Machine Vision Conference, Edinburgh, Scotland, 2006: 47-56.

ADAM A, RIVLIN E, and SHIMSHONI I. Robust fragments-based tracking using the integral histogram[C]. IEEE Conference on Computer Vision and Pattern Recognition, New York, USA, 2006: 798-805.

施引文献

资源附件(0)

访问统计