计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 271-276.doi: 10.11896/j.issn.1002-137X.2019.09.041
陈威1, 李决龙2, 邢建春1, 杨启亮1, 周启臻1
CHEN Wei1, LI Jue-long2, XING Jian-chun1, YANG Qi-liang1, ZHOU Qi-zhen1
摘要: 针对长时间目标跟踪中出现的目标形变、尺度变化、目标遮挡以及离开视野等问题,提出一种基于核相关滤波器和分层卷积特征的长时目标跟踪算法。首先,利用预训练的卷积神经网络模型提取分层卷积特征来训练核相关滤波器,进行位置估计。其次,构建目标尺度金字塔,进行尺度估计。最后,为了应对目标遮挡以及离开视野导致跟踪失败的情况,训练一个在线支持向量机进行目标再检测,从而实现长时间目标跟踪。在长时间目标跟踪数据集上的测试结果表明:所提算法的精度分别比其他几种主流跟踪算法HCF,LCT,DSST,KCF和TLD高出7%,15%,17%,21%和50%。
中图分类号:
[1]ORON S,BAR-HILLEL A,LEVI D,et al.Locally orderlesstracking[J].International Journal of Computer Vision,2015,111(2):213-228. [2]BABENKO B,YANG M H,BELONGIE S.Robust object trac-king with online multiple instance learning[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2011,33(8):1619-1632. [3]BOLME D S,BEVERIDGE J R,DRAPER B A,et al.Visual object tracking using adaptive correlation filters[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2010:2544-2550. [4]HENRIQUES J F,RUI C,MARTINS P,et al.Exploiting the circulant structure of tracking-by-detection with kernels[C]//European Conference on Computer Vision.Springer-Verlag,2012:702-715. [5]HENRIQUES J F,RUI C,MARTINS P,et al.High-speedtracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,37(3):583-596. [6]DANELLJAN M,HÄGER G,KHAN F S.Accurate scale estimation for robust visual tracking[C]//British Machine Vision Conference.2014:65.1-65.11. [7]KALAL Z,MIKOLAJCZYK K,MATAS J.Tracking-Learning-Detection[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,34(7):1409-22. [8]MA C,YANG X,ZHANG C,et al.Long-term correlation trac-king[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2015:5388-5396. [9]WANG N,YEUNG D Y.Learning a deep compact image representation for visual tracking[C]//International Conference on Neural Information Processing Systems.Curran Associates Inc.,2013:809-817. [10]WANG L,OUYANG W,WANG X,et al.Visual tracking with fully convolutional networks[C]//IEEE International Confe-rence on Computer Vision.IEEE Computer Society,2015:3119-3127. [11]MA C,HUANG J B,YANG X,et al.Hierarchical convolutional features for visual tracking[C]//IEEE International Conference on Computer Vision.IEEE,2016:3074-3082. [12]DENG J,DONG W,SOCHER R,et al.ImageNet:A large-scale hierarchical image database[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:248-255. [13]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014. [14]KUMAR B V K V,MAHALANOBIS A,JUDAY R D.Correlation Pattern Recognition[M].Cambridge University Press,2005. [15]MOUDGIL A,GANDHI V.Long-Term Visual Object Tracking Benchmark[J].arXiv:1712.01358,2017. [16]DANELLJAN M,HÄGER G,KHAN F,et al.Accurate scaleestimation for robust visual tracking[C]//British Machine Vision Conference.Nottingham:BMVA Press,2014. |
[1] | 单晓英, 任迎春. 基于改进麻雀搜索优化支持向量机的渔船捕捞方式识别 Fishing Type Identification of Marine Fishing Vessels Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm 计算机科学, 2022, 49(6A): 211-216. https://doi.org/10.11896/jsjkx.220300216 |
[2] | 陈景年. 一种适于多分类问题的支持向量机加速方法 Acceleration of SVM for Multi-class Classification 计算机科学, 2022, 49(6A): 297-300. https://doi.org/10.11896/jsjkx.210400149 |
[3] | 侯夏晔, 陈海燕, 张兵, 袁立罡, 贾亦真. 一种基于支持向量机的主动度量学习算法 Active Metric Learning Based on Support Vector Machines 计算机科学, 2022, 49(6A): 113-118. https://doi.org/10.11896/jsjkx.210500034 |
[4] | 邢云冰, 龙广玉, 胡春雨, 忽丽莎. 基于SVM的类别增量人体活动识别方法 Human Activity Recognition Method Based on Class Increment SVM 计算机科学, 2022, 49(5): 78-83. https://doi.org/10.11896/jsjkx.210400024 |
[5] | 武玉坤, 李伟, 倪敏雅, 许志骋. 单类支持向量机融合深度自编码器的异常检测模型 Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder 计算机科学, 2022, 49(3): 144-151. https://doi.org/10.11896/jsjkx.210100142 |
[6] | 侯春萍, 赵春月, 王致芃. 基于自反馈最优子类挖掘的视频异常检测算法 Video Abnormal Event Detection Algorithm Based on Self-feedback Optimal Subclass Mining 计算机科学, 2021, 48(7): 199-205. https://doi.org/10.11896/jsjkx.200800146 |
[7] | 郭福民, 张华, 胡瑢华, 宋岩. 一种基于表面肌电信号的腕部肌力估计方法研究 Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals 计算机科学, 2021, 48(6A): 317-320. https://doi.org/10.11896/jsjkx.200600021 |
[8] | 卓雅倩, 欧博. 噪声环境下的人脸防伪识别算法研究 Face Anti-spoofing Algorithm for Noisy Environment 计算机科学, 2021, 48(6A): 443-447. https://doi.org/10.11896/jsjkx.200900207 |
[9] | 雷剑梅, 曾令秋, 牟洁, 陈立东, 王淙, 柴勇. 基于整车EMC标准测试和机器学习的反向诊断方法 Reverse Diagnostic Method Based on Vehicle EMC Standard Test and Machine Learning 计算机科学, 2021, 48(6): 190-195. https://doi.org/10.11896/jsjkx.200700204 |
[10] | 王友卫, 朱晨, 朱建明, 李洋, 凤丽洲, 刘江淳. 基于用户兴趣词典和LSTM的个性化情感分类方法 User Interest Dictionary and LSTM Based Method for Personalized Emotion Classification 计算机科学, 2021, 48(11A): 251-257. https://doi.org/10.11896/jsjkx.201200202 |
[11] | 曹素娥, 杨泽民. 基于聚类分析算法和优化支持向量机的无线网络流量预测 Prediction of Wireless Network Traffic Based on Clustering Analysis and Optimized Support Vector Machine 计算机科学, 2020, 47(8): 319-322. https://doi.org/10.11896/jsjkx.190800075 |
[12] | 徐翔燕, 侯瑞环. 基于GM(1,1)-SVM组合模型的中长期人口预测研究 Medium and Long-term Population Prediction Based on GM(1,1)-SVM Combination Model 计算机科学, 2020, 47(6A): 485-487. https://doi.org/10.11896/JsJkx.190900168 |
[13] | 马创, 吕孝飞, 梁炎明. 基于GA-SVM的农产品质量分类 Agricultural Product Quality Classification Based on GA-SVM 计算机科学, 2020, 47(6A): 517-520. https://doi.org/10.11896/JsJkx.190900184 |
[14] | 宋岩, 胡瑢华, 郭福民, 袁新亮, 熊睿洋. 基于sEMG的改进SVM+BP肌力预测分层算法 Improved SVM+BP Algorithm for Muscle Force Prediction Based on sEMG 计算机科学, 2020, 47(6A): 75-78. https://doi.org/10.11896/JsJkx.190900143 |
[15] | 方梦琳, 唐文兵, 黄鸿云, 丁佐华. 基于模糊信息分解与控制规则的移动机器人沿墙导航 Wall-following Navigation of Mobile Robot Based on Fuzzy-based Information Decomposition and Control Rules 计算机科学, 2020, 47(6A): 79-83. https://doi.org/10.11896/JsJkx.191000158 |
|