-
摘要:
针对多示例学习（MIL）跟踪算法在包概率计算过程中对示例样本不加以区分导致分类器性能下降，及采用最大化似然函数选择相应的弱分类构造强分类增加了算法复杂度的问题，提出了一种基于目标性权值学习的多示例目标跟踪算法，该算法利用目标性测量每个示例样本对包概率的重要性，根据其目标性测量结果对每个正示例样本赋予相应的权值，从而判别性地计算包概率，提高跟踪精度。同时在弱分类器选择过程中，采用最大化弱分类器与似然函数概率内积的方法从弱分类器池中选择弱分器构造强分类器，减少算法的计算复杂度。通过对不同复杂场景下视频序列的跟踪，实验结果表明，本文所提出的目标性权值学习的多示例目标跟踪算法优于其对比算法，表现出较好的跟踪精度和鲁棒性能。
-
关键词:
- 多示例学习（MIL） /
- 目标性测量 /
- 弱分类器选择 /
- 包概率计算 /
- 目标跟踪
Abstract:For the problems that the multiple instance learning (MIL) tracking algorithm does not distinguish the differences of each sample when computing the bag probability and selects the weak classifiers by maximizing the log likelihood function, which reduce the performance of classifier and increase the complexity of the algorithm, this paper proposes a tracking algorithm based on objectness weighted multiple instance learning. First, the importance of each sample is measured by the objectness, which is also used to assign the weight for each instance. Then the weighted value is utilized for computing the final bag probability. In the phase of weak classifier selection, a maximized inner product between weak classifier and log likelihood function is adopted to select weak classifiers from weak classifier pool, and then these weak classifiers are combined into a strong classifier. All these strategies are beneficial for improving the tracking accuracy and reducing the computational complexity. By tracking the video sequences under different complex scenes, experimental results show that the proposed algorithm has strong robustness and high tracking accuracy compared with competing method.
-
图 1 正负样本示例及其NG特征
Figure 1. Positive and negative sample instances and their NG features
图 2 ℓ1regularized SVM分类器正样本获取方法
Figure 2. Acquisition method of positive sample for ℓ1regularized SVM classifier
图 6 对遮挡、旋转、尺度及光照变化视频序列的跟踪结果
Figure 6. Tracking results for video sequences with occlusion, rotation, scale and illumination changes
图 7 对遮挡、快速运动及复杂背景视频序列的跟踪结果
Figure 7. Tracking results for video sequences with occlusion, fast motion and background clutters
表 1 测试视频序列的特点
Table 1. Characteristics of test video sequences
视频序列 帧数 主要特点 david1 471 遮挡、尺度、旋转及光照变化 trellic 569 旋转、尺度及光照变化，复杂背景 Shaking 365 光照、尺度变化，旋转及复杂背景 Tiger2 365 遮挡，快速运动，复杂背景 deer 71 运动模糊、形变、旋转及复杂背景 carDark 393 光照变化及复杂背景 
下载: 导出CSV
-
[1] WU Y, LIM J, YANG M H.Online object tracking:A benchmark[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2013:2411-2418. [2] ZHANG K, ZHANG L, YANG M H.Fast compressive tracking[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 36(10):2002-2015. http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?reload=true&arnumber=6784124&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_Publication_Number%3A34%29%26pageNumber%3D40%26rowsPerPage%3D50 [3] ZHONG W, LU H, YANG M H.Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5):2356-2368. doi: 10.1109/TIP.2014.2313227 [4] ROSS D A, LIM J, LIN R S, et al.Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1-3):125-141. doi: 10.1007/s11263-007-0075-7 [5] ADAM A, RIVLIN E, SHIMSHONI I.Robust fragments-based tracking using the integral histogram[C]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2006:798-805. [6] KWON J, LEE K M.Visual tracking decomposition[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2010:1269-1276. [7] KALAL Z, MIKOLAJCZYK, MATAS J.Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 34(7):1409-1422. http://ieeexplore.ieee.org/document/6104061/authors [8] GRABNER H, BISCHOF H.On-line boosting and vision[C]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Piscataway, NJ:IEEE Press, 2006:260-267. [9] GRABNER H, LEISTNER C, BISCHOF H.Semi-supervised on-line boosting for robust tracking[C]//Computer Vision-ECCV 2008, European Conference on Computer Vision.Berlin:Springer, 2008:234-247. [10] BABENKO B, YANG M H, BELONGIE S.Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(8):1619-1632. http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?reload=true&arnumber=5674053&filter%3DAND%28p_IS_Number%3A5898466%29 [11] ZHANG K, ZHANG L, LIU Q, et al.Fast visual tracking via dense spatio-temporal context learning[M]. Berlin:Springer, 2014:127-141. [12] ZHANG K, SONG H.Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1):397-411. doi: 10.1016/j.patcog.2012.07.013 [13] 丁建睿, 黄剑华, 刘家锋, 等.局部特征与多示例学习结合的超声图像分类方法[J].自动化学报, 2013, 39(6):861-867. http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201306020.htmDING J R, HUANG J H, LIU J F, et al.Combining local features and multi-instance learning for ultrasound image classification[J]. Acta Automatica Sinica, 2013, 39(6):861-867(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201306020.htm [14] VIOLA P, JONES M.Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001.Piscataway, NJ:IEEE Press, 2001:511. [15] CHENG M M, ZHANG Z, LIN W Y, et al.BING:Binarized normed gradients for objectness estimation at 300fps[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway, NJ:IEEE Press, 2014:3286-3293. [16] LIANG P, LIAO C, MEI X, et al.Adaptive objectness for object tracking[J]. IEEE Signal Processing Letters, 2016, 23(7):949-953. doi: 10.1109/LSP.2016.2556706 [17] YUAN G X, CHANG K W, HSIEH C J, et al.A comparison of optimization methods and software for large-scale L1-regularized linear classification[J]. Journal of Machine Learning Research, 2010, 11(2):3183-3234. http://portal.acm.org/citation.cfm?id=1953034 [18] XU X, FRANK E.Logistic regression and boosting for labeled bags of instances[J]. Lecture Notes in Computer Science, 2004, 3056(3):272-281. [19] EVERINGHAM M, GOOL L V, WILLIAMS C K I, et al.The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2):303-338. doi: 10.1007/s11263-009-0275-4 -
下载:
点击查看大图
计量
- 文章访问数: 751
- HTML全文浏览量: 68
- PDF下载量: 458
- 被引次数: 0
