跟踪-学习-检测框架下改进加速梯度的目标跟踪

doi:10.13229/j.cnki.jdxbgxb20160411

吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (2): 533-538.doi: 10.13229/j.cnki.jdxbgxb20160411

跟踪-学习-检测框架下改进加速梯度的目标跟踪

杨欣^{1, 2}, 夏斯军¹, 刘冬雪¹, 费树岷³, 胡银记²

1.南京航空航天大学自动化学院,南京 210016;
2.光电控制技术重点实验室,河南洛阳 471000;
3.东南大学自动化学院, 南京 210096

收稿日期:2016-04-05 出版日期:2018-03-01 发布日期:2018-03-01
作者简介:杨欣(1978-),男,副教授,博士. 研究方向:模式识别,图像处理. E-mail:yangxin@nuaa.edu.cn
基金资助:
国家自然科学基金项目(61573182,61172135,61101198); 光电控制技术重点实验室和航空科学基金联合项目(20145152027)

Target tracking based on improved accelerated gradient under tracking-learning-detection framework

YANG Xin^{1, 2}, XIA Si-jun¹, LIU Dong-xue¹, FEI Shu-min³, HU Yin-ji²

1.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2.Key Laboratory of Photoelectric Control Technology, Luoyang 471000, China;
3.School of Automation, Southeast University, Nanjing 210096, China

Received:2016-04-05 Online:2018-03-01 Published:2018-03-01

摘要/Abstract

摘要： 单目标持久跟踪的主要难点是由于目标姿态、相似背景及遮挡等因素而导致的漂移问题。基于此提出了一种改进L1APG (L1 tracker using accelerated proximal gradient approach) 的目标-学习-检测(TLD)目标跟踪算法。首先,在L1APG跟踪器中加入遮挡检测判断;其次,将遮挡程度转换为目标模板和背景模板系数的权重;最后,用改进的L1APG跟踪器取代传统TLD框架中的跟踪器,自适应地根据遮挡程度改变模板系数,从而有效地提高了跟踪效果。实验表明:本文算法与传统TLD跟踪框架相比,能更好地处理遮挡和漂移问题,具有较好的稳定性和鲁棒性。

关键词: 人工智能, 目标跟踪, 目标-学习-检测, 遮挡, 漂移

Abstract: The main challenges in a single target persistent tracking are the factors such as the change of the target pose, similar background and occlusion, which account for the difficulty in solving the drift problem. Based on Tracking-Learning-Detection (TLD), we propose an improved Li Tracker Using Accelerated Proximal Gradient Approach (L1APG) tracking algorithm. First, we add occlusion detection to the L1APG tracker. Then, we transform the occlusion problem into the weight of the target template and the background template coefficients. Finally, the original tracker is replaced by the improved L1APG tracker in the traditional TLD algorithm, the coefficient weights vary adaptively according to the degree of occlusion in real time, which contributes to improving the tracking results effectively. Experiments show that, compared with TLD and L1 tracking algorithms, the proposed algorithm can better deal with the occlusion and drift problems and possesses better stability and robustness.

Key words: artificial intelligence, target tracking, tracking-learning-detection (TLD), occlusion, drift

中图分类号:

TP391.41

杨欣, 夏斯军, 刘冬雪, 费树岷, 胡银记. 跟踪-学习-检测框架下改进加速梯度的目标跟踪[J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.

YANG Xin, XIA Si-jun, LIU Dong-xue, FEI Shu-min, HU Yin-ji. Target tracking based on improved accelerated gradient under tracking-learning-detection framework[J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.

参考文献

[1] Beaudry C, Péteri R, Mascarilla L. Action recognition in videos using frequency analysis of critical point trajectories[C]∥2014 IEEE International Conference on Image Processing (ICIP), Paris, France, 2014: 1445-1449.
[2] Dondo D G, Redolfi J A, Griffa M, et al. Target tracking system using multiple cameras and Bayesian estimation[J]. IEEE Latin America Transactions, 2016, 14(6): 2713-2718.
[3] 郝志成,高文. 多模跟踪技术在轮式侦察车图像处理器的应用[J]. 中国光学, 2011, 4(5): 480-488.
Hao Zhi-cheng, Gao Wen. Application of multi-pattarn tracking in wheel type scout car image processor[J]. Chinese Optics, 2011, 4(5): 480-488.
[4] 熊晶莹, 戴明. 适应移动智能设备的目标跟踪器[J]. 光学精密工程, 2017,25(12):3152-3159.
Xiong Jing-ying, Dai Ming. Design of tracker for mobile smart devices[J]. Optics and Precision Engineering, 2017, 25(12): 3152-3159.
[5] Buehler P, Everingham M, Huttenlocher D P, et al. Long term arm and hand tracking for continuous sign language TV broadcasts[C]∥Proceedings of the 19th British Machine Vision Conference, Leeds, UK, 2008: 1105-1114.
[6] Xu J, Li J. Moving target tracking algorithm based on scale invariant optical flow method[C]∥ Information Science and Control Engineering (ICISCE), Beijing, China, 2016: 468-472.
[7] 杨欣,费树岷,李刚,等. 基于复杂特征融合的改进mean shift目标跟踪[J]. 控制与决策,2014(7):1297-1300.
Yang Xin,Fei Shu-min, Li Gang, et al. Improved mean shift target tracking based on complex feature fusion[J]. Control and Decision,2014(7):1297-1300.
[8] 王田, 刘伟宁, 韩广良, 等.基于改进MeanShift的目标跟踪算法[J]. 液晶与显示, 2012,27(3): 396-400.
Wang Tian, Liu Wei-ning, Han Guang-liang, et al. Target tracking algorithm based on improved Meanshift[J].Chinese Journal of Liquid Crystals and Displays,2012,27(3):396-400.
[9] 许佳佳.基于MeanShift算法的航空影像联合分割[J].液晶与显示, 2014,29(4): 586-591.
Xu Jia-jia. Joint segmentation using MeanShift algorithm for high resolution aerial images[J]. Chinese Journal of Liquid Crystals and Displays, 2014,29(4): 586-591.
[10] 夏楠, 邱天爽, 李景春, 等. 一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法[J]. 电子学报, 2013, 41(1): 148-152.
Xia Nan,Qiu Tian-shuang, Li Jing-chun, et al. A nonlinear filtering algorithm combining Kalman filter with particle filter[J]. Journal of Electronics, 2013, 41 (1): 148-152.
[11] 杜超, 刘伟宁, 刘恋.一种基于卡尔曼滤波及粒子滤波的目标跟踪算法[J].液晶与显示, 2011,26(3): 384-389.
Du Chao, Liu Wei-ning, Liu Lian. Target tracking algorithm based on Kalman filter and particle filter[J]. Chinese Journal of Liquid Crystals and Displays, 2011,26(3): 384-389.
[12] Mei X, Ling H. Robust visual track-ing using ? 1 minimization[C]∥IEEE International Conference on Computer Vision, Xi'an, China, 2009: 1436-1443.
[13] 张亚红,杨欣,沈雷,等.基于视觉显著特征的自适应目标跟踪[J]. 吉林大学学报:信息科学版,2015, 33(2):195-200.
Zhang Ya-hong, Yang Xin, Shen Lei, et al. Adaptive target tracking based on salient features of vision [J]. Journal of Jilin University (Information Science Edition), 2015, 33(2) :195-200.
[14] 李静宇,王延杰.基于子空间的目标跟踪算法研究[J].液晶与显示, 2014,29(4): 617-622.
Li Jing-yu, Wang Yan-jie. Subspace based target tracking algorithm[J]. Chinese Journal of Liquid Crystals and Displays, 2014,29(4): 617-622.
[15] Zhang K, Zhang L, Yang M H. Real-time Compressive Tracking[M].Florence, Italy:Springer, 2012: 864-877.
[16] 杨欣,刘加,周鹏宇,等. 基于多特征融合的粒子滤波自适应目标跟踪算法[J]. 吉林大学学报:工学版,2015,45(2):533-539.
Yang Xin, Liu Jia, Zhou Peng-yu,et al. Particle filter adaptive target tracking algorithm based on multi-featurefusion[J]. Journal of Jilin University(Engineering and Technology Edition), 2015,45 (2):533-539.
[17] 王暐,王春平,李军,等. 特征融合和模型自适应更新相结合的相关滤波目标跟踪[J].光学精密工程, 2016, 24(8): 2059-2066.
Wang Wei,Wang Chun-ping,Li Jun, et al. Correlation filter tracking based on feature fusing and model adaptive updating[J]. Optics and Precision Engineering, 2016, 24(8): 2059-2066.
[18] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Trans Pattern Anal Mach Intell, 2012, 34(7): 1409-1422.
[19] Kalal Z, Mikolajczyk K, Matas J. Forward-backward error: automatic detection of tracking failures[C]∥ International Conference on Pattern Recognition,Japan, 2010:2756-2759.
[20] 周鑫, 钱秋朦, 叶永强, 等. 改进后的 TLD 视频目标跟踪方法 [J]. 中国图象图形学报, 2013, 18(9): 1115-1123.
Zhou Xin, Qian Qiu-meng, Ye Yong-qiang, et al. The improved TLD video target tracking method[J]. Journal of Image and Graphics,2013, 18(9): 1115-1123.

相关文章 15

[1]	董飒, 刘大有, 欧阳若川, 朱允刚, 李丽娜. 引入二阶马尔可夫假设的逻辑回归异质性网络分类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1571-1577.
[2]	顾海军, 田雅倩, 崔莹. 基于行为语言的智能交互代理[J]. 吉林大学学报(工学版), 2018, 48(5): 1578-1585.
[3]	王旭, 欧阳继红, 陈桂芬. 基于垂直维序列动态时间规整方法的图相似度度量[J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[4]	张浩, 占萌苹, 郭刘香, 李誌, 刘元宁, 张春鹤, 常浩武, 王志强. 基于高通量数据的人体外源性植物miRNA跨界调控建模[J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[5]	黄岚, 纪林影, 姚刚, 翟睿峰, 白天. 面向误诊提示的疾病-症状语义网构建[J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[6]	李雄飞, 冯婷婷, 骆实, 张小利. 基于递归神经网络的自动作曲算法[J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7]	刘杰, 张平, 高万夫. 基于条件相关的特征选择方法[J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[8]	王旭, 欧阳继红, 陈桂芬. 基于多重序列所有公共子序列的启发式算法度量多图的相似度[J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[9]	刘雪娟, 袁家斌, 许娟, 段博佳. 量子k-means算法[J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[10]	曲慧雁, 赵伟, 秦爱红. 基于优化算子的快速碰撞检测算法[J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[11]	李嘉菲, 孙小玉. 基于谱分解的不确定数据聚类方法[J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[12]	邵克勇, 陈丰, 王婷婷, 王季驰, 周立朋. 无平衡点分数阶混沌系统全状态自适应控制[J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
[13]	王生生, 王创峰, 谷方明. OPRA方向关系网络的时空推理[J]. 吉林大学学报(工学版), 2017, 47(4): 1238-1243.
[14]	马淼, 李贻斌. 基于多级图像序列和卷积神经网络的人体行为识别[J]. 吉林大学学报(工学版), 2017, 47(4): 1244-1252.
[15]	周炳海, 彭涛. 混流装配线准时化物料配送调度优化[J]. 吉林大学学报(工学版), 2017, 47(4): 1253-1261.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

跟踪-学习-检测框架下改进加速梯度的目标跟踪

Target tracking based on improved accelerated gradient under tracking-learning-detection framework

RICH HTML

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 3