|
|
|
| Object Tracking via Global and Local Structural Inverse Sparse Appearance Model |
| HU Zhengping, XIE Ronglu, WANG Meng, SUN Zhe |
| School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004 |
|
|
|
|
Abstract To improve the performance of sparse representation based trackers, an object tracking method based on global and local structural inverse sparse appearance model is proposed. Firstly, an inverse sparse representation formulation is proposed to compute the weights of all particles by solving one optimization problem and this is conducive to improving the real-time performance. Then, a ranking mechanism based on joint discriminative similarity map(JDS map) is designed to improve the robustness. The formulation block candidates are divided into several pitches and the weighted sparse solutions are computed respectively. Next, these pitches are concatenated with different weights and meanwhile the sparse solution of each particle is computed. A combination mechanism is proposed to unite two sparse solutions as the JDS map. During the tracking, the object template and weight template are updated using a double-template updating strategy. Experiments demonstrate that the proposed algorithm is robust for benchmark video sequences under complicated conditions.
|
|
Received: 20 April 2016
|
|
|
| Fund:Supported by National Natural Science Foundation of China (No.61071199), Natural Science Foundation of Hebei Province (No.F2016203422) |
About author:: (HU Zhengping(Corresponding author), born in 1970, Ph.D., professor. His research interests include pattern recognition theory and its applications.)(XIE Ronglu, born in 1992, master student. Her research interests include target tracking.)
(WANG Meng, born in 1982, Ph.D. candidate. Her research interests include pattern recognition and sparse representation classification.)(SUN Zhe, born in 1990, Ph.D. candidate. Her research interests include sparse representation classification.) |
|
|
|
[1] LI A N, LIN M, WU Y, et al. NUS-PRO: A New Visual Tracking Challenge. IEEE Trans on Pattern Analysis and Machine Intelligence, 2016, 38(2): 335-349.
[2] BO C J, ZHANG J X, HE J J, et al. Online Objecting Tracking via
Bounded Error Distance. Neurocomputing, 2016, 171(C): 1175-1184.
[3] WANG D, LU H C, YANG M H. Online Object Tracking with Sparse Prototypes. IEEE Trans on Image Processing, 2013, 22(1): 314-325.
[4] WANG B X, TANG L B, YANG J L, et al. Visual Tracking Based on Extreme Learning Machine and Sparse Representation. Sensors, 2015, 15(10): 26877-26905.
[5] ZHANG T Z, LIU S, AHUJA N, et al. Robust Visual Tracking via Consistent Low-Rank Sparse Learning. International Journal of Computer Vision, 2014, 111(2): 171-190.
[6] MEI X, LIU H B. Robust Visual Tracking Using l1 Minimization // Proc of the 12th IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2009: 1436-1443.
[7] LIU B Y, YANG L, HUANG J Z, et al. Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization // Proc of the 11th European Conference on Computer Vision. Berlin, Germany: Springer, 2010, IV: 624-637.
[8] BAO C L, WU Y, LING H B, et al. Real Time Robust L1 Tracker Using Accelerated Proximal Gradient Approach // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2012: 1830-1837.
[9] BAI T X, LI Y F. Robust Visual Tracking with Structured Sparse Representation Appearance Model. Pattern Recognition, 2012, 45(6): 2390-2404.
[10] LIU H P, SUN F C. Visual Tracking Using Sparsity Induced Similarity // Proc of the 20th International Conference on Pattern Re-cognition. Washington, USA: IEEE, 2010: 1702-1705.
[11] ZHANG T Z, GHANEM B, LIU S, et al. Robust Visual Tracking via Multi-task Sparse Learning // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2012: 2042-2049.
[12] ZHANG T Z, GHANEM B, LIU S, et al. Robust Visual Tracking via Structured Multi-task Sparse Learning. International Journal of Computer Vision, 2013, 101(2): 367-383.
[13] WANG D, LU H C, XIAO Z Y, et al. Inverse Sparse Tracker with a Locally Weighted Distance Metric. IEEE Trans on Image Proce-ssing, 2015, 24(9): 2646-2657.
[14] KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-Learning-Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.
[15] 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.
[16] LIU B Y, HUANG J Z, YANG L, et al. Robust Tracking Using Local Sparse Appearance Model and K-Selection // Proc of the 24th IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2011: 1313-1320.
[17] 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.
[18] KALAL Z, MATAS J, MIKOLAJCZYK K. P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints // Proc of the 23rd IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2010: 49-56.
[19] KWON J, LEE K M. Visual Tracking Decomposition // Proc of the 23rd IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2010: 1269-1276.
[20] 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.
[21] ADAM A, RIVLIN E, SHIMSHONI I. Robust Fragments-Based Tracking Using the Integral Histogram // Proc of the 19th IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2006, I: 798-805.
[22] ZOU H, HASTIE T, TIBSHIRANI R. Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics, 2006, 15(2): 265-286. |
|
|
|
2016, Vol. 29 
