Compressive Tracking Algorithm Based on Particle Filter and Sample Weighting

ZHANG Hongying; WANG Sainan; HU Wenbo

doi:10.11999/JEIT170854

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1397-1403

ZHANG Hongying, WANG Sainan, HU Wenbo. Compressive Tracking Algorithm Based on Particle Filter and Sample Weighting[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1397-1403. doi: 10.11999/JEIT170854

Citation:

ZHANG Hongying, WANG Sainan, HU Wenbo. Compressive Tracking Algorithm Based on Particle Filter and Sample Weighting[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1397-1403. doi: 10.11999/JEIT170854

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Compressive Tracking Algorithm Based on Particle Filter and Sample Weighting

doi: 10.11999/JEIT170854

Funds:

The Natural Science Foundation of Tianjin (12JCQNJC00600), The Fundamental Research Funds for the Central Universities (3122015C016), The National Natural Science Foundation of China (U1533203)

Received Date: 2017-09-07
Rev Recd Date: 2018-01-31
Publish Date: 2018-06-19

Abstract

Abstract

To solve the problem that Compressive Tracking (CT) algorithm is unable to adapt to the scale change of the object and ignores the sample weight, an optimized compressive tracking algorithm based on particle filter and sample weighting is presented. Firstly, the compressive feature is improved for building a target apparent model with normalized rectangle features. Then, the thought of sample weighting is utilized. In order to increase the precision of the classifier, different weights are given to the positive samples in accordance with the different distances between the positive samples and the object. Finally, the dynamic state estimation is made under the particle filter frame with integrating the scale invariant feature. At the phase of particle prediction, a second-order autoregressive model is utilized to obtain the estimation and prediction of the particle state. The particle state is updated with the observation model. The particles resampling is used to prevent the degradation of particles. Experimental results demonstrate that the improved algorithm can adapt to the scale change of object, and the accuracy and stability of the compressive tracking algorithm is improved.
- Compressive Tracking (CT),
- Particle filter,
- Sample weighting,
- Classifier

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References(19)

References

黎万义, 王鹏, 乔红. 引入视觉注意机制的目标跟踪方法综述[J]. 自动化学报, 2013, 40(4): 561-576. doi: 10.3724/SP.J. 1004.2014.00561.

LI Wanyi, WANG Peng, and QIAO Hong. A survey of visual attention based methods for object tracking[J]. Acta Automatica Sinica, 2013, 40(4): 561-576. doi: 10.3724/SP.J. 1004.2014.00561.

MEI Xue and LING Haibin. Robust visual tracking and vehicle classification via spare representation[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2011, 33(11): 2259-2272. doi: 10.1109/TPAMI.2011.66.

BABENKO B, YANG Minghsuan, and BELONGIE S. Visual tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2009, 33(8): 983-990. doi: 10.1109/CVPR.2009.5206737.

KALAL Z, MIKOLAJCZYK K, and MATAS J. Tracking- learning-detection[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2012, 34(7): 1409-1422. doi: 10.1109/TPAMI.2011.239.

MAO Jiansen and QU Yufu. Tracking of variable scale object based on compressive sensing[J]. Chinese Journal of Liquid Crystals Displays, 2016, 31(6): 497-505. doi: 10.3788/ YJYXS20163105.0497.

ESLAHI N and AGHAGOLZADEH A. Compressive sensing image restoration using adaptive curvelet thresholding and nonlocal sparse regularization[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3126-3140. doi: 10.1109/TIP. 2016.2562563.

ZHANG Kaihua, ZHANG Lei, and YANG Minghsuan. Real-time compressive tracking[C]. Proceedings of the 12th European Conference on Computer Vision, Florence, Italy, 2012: 864-877. doi: 10.1007/978-3-642-33712-3_62.

ZHANG Lei, WANG Yanjie, and HE Shuwen. Real-time compressive tracking method based on phase congruency[J]. Acta Photonica Sinica, 2014, 43(8): 124-131 doi: 10.3788/ gzxb20144308.0810003.

郑超, 陈杰, 殷松峰, 等. 改进的协同训练框架下压缩跟踪[J]. 电子与信息学报, 2016, 38(7): 1624-1630. doi: 10.11999/ JEIT151001.

ZHENG Chao, CHEN Jie, YIN Songfeng, et al. Optimized compressive tracking in co-training framework[J]. Journal of Electronics Information Technology, 2016, 38(7): 1624-1630. doi: 10.11999/JEIT151001.

崔灿, 王民钢, 李立, 等. 改进压缩特征的实时压缩跟踪算法[J]. 计算机工程与应用, 2017, 53(15): 210-216. doi: 10.3778/ j.issn.1002-8331.1603-0060.

CUI Can, WANG Mingang, LI Li, et al. Real-time compressive tracking with advanced compression features[J]. Computer Engineering and Applications, 2017, 53(15): 210-216. doi: 10.3778/j.issn.1002-8331.1603-0060.

GHIRMIA T. Distributed particle filter for object tracking: with reduced sensor communications[J]. Sensors, 2016, 16(9): 1454-1464. doi: 10.3390/s16091454.

ADAM A, RIVLIN E, and SHIMSHONI I. Robust fragments- based tracking using the integral histogram[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, 2006: 798-805. doi: 10.1109/ CVPR.2006.256.

NING J, SHI W, YANG S, et al. Visual tracking based on distribution fields and online weighted multiple instance learning[J]. Image Vision Computing, 2013, 31(11): 853-863. doi: 10.1016/j.imavis.2013.09.003.

ORON S, BAARHILLEL A, and LEVI D. Locally orderless tracking[J]. International Journal of Computer Vision, 2015, 111(2): 213-228. doi: 10.1109/CVPR.2012.6247895.

GRABNER H, GRABNER M, and BISCHOF H. Real-time tracking via on-line boosting[C]. Proceedings of the British Machine Vision Conference, Edinburgh, UK, 2006, 1: 47-56. doi: 10.5244/C.20.6.

WU Y, 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. doi: 10.1109/CVPR.2013.312.

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