Abstract：Compressive tracking algorithms based on compressive sensing theory for reducing the dimension of Haar-like feature of the target utilize a fixed tracking scale, and therefore they are prone to tracking drift or even target missing when the size of the target changes. To overcome the drawback, the variation of Haar-like feature according to the target scales is analyzed. It is found that the values of Haar-like feature of target in the tracking rectangular frame change with the area of the tracking frame in an approximately linear way within certain range of scales. Grounded on this relationship, an improved compressive tracking algorithm adapting to variable target scales (CTVS) is proposed. Experimental results show that CTVS can adapt to the change of target size and perform well in reducing the influence of interferences like occlusion, light illumination variation, background clutter and deformation. Moreover, CTVS is capable of real-time tracking with higher robustness, accuracy and computation efficiency.
[1] 张焕龙,胡士强,杨国胜.基于外观模型学习的视频目标跟踪方法综述.计算机研究与发展, 2015, 52(1): 177-190. (ZHANG H L, HU S Q, YANG G S. Video Object Tracking Based on Appearance Models Learning. Journal of Computer Research and Development, 2015, 52(1): 177-190.) [2] WU Y, LIM J, YANG M H. Online Object Tracking: A Benchmark // Proc of the IEEE Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2013: 2411-2418. [3] 黄凯奇,陈晓棠,康运锋,等.智能视频监控技术综述.计算机学报, 2015, 38(6): 1093-1118. (HUANG K Q, CHEN X T, KANG Y F, et al. Intelligent Visual Surveillance: A Review. Chinese Journal of Computers, 2015, 38(6): 1093-1118.) [4] 李庆武,朱国庆,周 妍,等.基于特征在线选择的目标压缩跟踪算法.自动化学报, 2015, 41(11): 1961-1970. (LI Q W, ZHU G Q, ZHOU Y, et al. Object Compressive Tracking via Online Feature Selection. Acta Automatica Sinica, 2015, 41(11): 1961-1970.) [5] SMEULDERS A W M, CHU D M, CUCCHIARA R, et al. Visual Tracking: An Experimental Survey. IEEE Trans on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1442-1468. [6] MEI X, LING H B. Robust Visual Tracking and Vehicle Classification via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2259-2272. [7] BLACK M J, JEPSON A D. EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation. International Journal of Computer Vision, 1998, 26(1): 63-84. [8] ROSS D A, LIM J, LIN R S, et al. Incremental Learning for Robust Visual Tracking. International Journal of Computer Vision, 2008, 77(1): 125-141. [9] LI H X, SHEN C H, SHI Q F. Real-Time Visual Tracking Using Compressive Sensing // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2011: 1305-1312. [10] GRABNER H, GRABNER M, BISCHOF H. Real-Time Tracking via On-line Boosting [C/OL]. [2016-04-25]. http://www.vision.ee.ethz.ch/~hegrabne/papers/Grabner2006Real-TimeTrackingvia.pdf [11] GRABNER H, LEISTNER C, BISCHOF H. Semi-supervised On-line Boosting for Robust Tracking // Proc of the European Confe-rence on Computer Vision. Berlin, Germany: Springer-Verlag, 2008: 234-247. [12] BABENKO B, YANG M H, BELONGIE S. Robust Object Tracking with Online Multiple Instance Learning. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632. [13] KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-Learning-Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 34(7): 1409-1422. [14] HARE S, SAFFARI A, TORR P H S. Struck: Structured Output Tracking with Kernels. IEEE Trans on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096-2109. [15] COLLINS R T, LIU Y X, LEORDEANU M. On-line Selection of Discriminative Tracking Features. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1631-1643. [16] 齐美彬,陆 磊,杨 勋,等.基于自适应压缩特征选择的实时目标跟踪算法.模式识别与人工智能, 2015, 28(4): 361-368. (QI M B, LU L, YANG X, et al. Real-Time Object Tracking Algorithm Based on Adaptive Compressive Feature Selection. Pattern Recognition and Artificial Intelligence, 2015, 28(4): 361-368.) [17] ZHANG K H, ZHANG L, YANG M H. Real-Time Compressive Tracking // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer-Verlag, 2012: 864-877. [18] ZHANG K H, ZHANG L, YANG M H. Fast Compressive Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002-2015. [19] DONOHO D L. Compressed Sensing. IEEE Trans on Information Theory, 2006, 52(4): 1289-1306. [20] BARANIUK R, DAVENPORT M, DEVORE R, et al. A Simple Proof of the Restricted Isometry Property for Random Matrices. Constructive Approximation, 2008, 28(3): 253-263. [21] ACHLIOPTAS D. Database-Friendly Random Projections // Proc of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. New York, USA: ACM, 2001: 274-281. [22] VIOLA P, JONES M. Rapid Object Detection Using a Boosted Ca-scade of Simple Features // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2001, I: 511-519. [23] NG A Y, JORDAN M I. On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes // BECKER S, THRUN S, OBERMAYER K, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2002: 169-187. [24] DIACONIS P, FREEDMAN D. Asymptotics of Graphical Projection Pursuit. The Annals of Statistics, 1984, 12(3): 793-815. [25] ZHANG K H, ZHANG L, LIU Q S, et al. Fast Visual Tracking via Dense Spatio-Temporal Context Learning // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 127-141.
2016, Vol. 29 
