期刊文献+

基于改进的随机森林TLD目标跟踪方法 被引量:1

TLD Target Tracking Method based on Improved Random Forest
下载PDF
导出
摘要 针对TLD算法中采用的随机森林分类器的决策树阈值固定,不能根据目标特征随时调整,影响分类精度和时间开销的问题,引入极端随机森林的思想,提出了基于改进的随机森林TLD目标跟踪方法。该方法用Gini系数度量样本集合的混乱程度,通过比较Gini系数是否超过了给定阈值,判断叶节点何时转变成决策节点进行分裂;再结合TLD算法中的P-N学习框架和在线模型训练更新样本;最终基于改进的TLD算法完成目标跟踪。将本文方法应用于多个视频集进行目标跟踪实验,验证了算法的有效性和稳定性。 The fixed threshold value of the random forest classifier can not be readily adapted to the target feature of TLD (Tracking Learning Detecting) algorithm, which affects classification accuracy and time overhead issues. Aiming at this problem, an idea of extreme random forest was introduced, and a TLD target tracking method based on improved random forest is proposed. This method used Gini coefficient to measure the degree of confusion of sample sets, by deciding the Gini coefficient whether exceeded a given threshold to judge when a leaf node changed into the decision node to split. Combining P - N learning framework and online model, samples were trained and updated. Finally, the target tracking was completed based on improved TLD algorithm. The proposed method was used in many video sets for target tracking to verify its effectiveness and stability.
出处 《大连民族大学学报》 2016年第3期255-259,共5页 Journal of Dalian Minzu University
基金 中央高校基本科研业务费专项资金资助项目(DC201502060303 DC201501075)
关键词 目标跟踪 随机森林 TLD 分裂阈值 在线学习 target tracking random forests TLD split threshold online learning
  • 相关文献

参考文献3

二级参考文献32

  • 1王涛,李舟军,胡小华,颜跃进,陈火旺.一种高效的数据流挖掘增量模糊决策树分类算法[J].计算机学报,2007,30(8):1244-1250. 被引量:18
  • 2ROSS D A, LIM J, LIN R-S, et al. Incremental learning for robust visual tracking [ J]. International Journal of Computer Vision, 2009, 77(1/2/3) : 125 - 141.
  • 3ZHANG L, ZHANG K, YANG M. Real-time compressive tracking [ C]// ECCV 2012: Proceedings of the 12th European Conference on Computer Vision, LNCS 7574. Berlin: Springer, 2012: 866- 879.
  • 4KWON J, LEE K M. Visual tracking decomposition [ C]//CVPR 2010: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2010:1269-1276.
  • 5MEI X, LING H. Robust visual tracking using 11 minimization [ C]//ICCV 2009: Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. Piscataway: IEEE, 2009:1436 - 1443.
  • 6KALAL Z, MATAS J, MIKOLAJCZYK K. P-N learning: bootstrap- ping binary classifiers by structural constraints [ C]// CVPR 2010: Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2010:49-56.
  • 7ZHOU Q-H, LU H, YANG M-H. Online multiple support instance tracking [C]// FG 2011: Proceedings of the 2011 IEEE Interna- tional Conference on on Automatic Face and Gesture Recognition. Piscataway: IEEE, 2011 : 545 - 552.
  • 8LEISTINER C, GODEC M, SAFFARI A, et al. Online multi-view forests for tracking [ C]//Proceedings of the 32nd DAGM Symposi-um on Pattern Recognition, LNCS 6376. Bedim Springer, 2010: 493 - 502.
  • 9BABENKO B, YANG M-H, BELONGIE S. Robust object tracking with online multiple instance learning [ J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33 (8) : 1619 - 1632.
  • 10NING J, SHI W, YANG S, et al. Improved appearance updating method in multiple instance learning tracking [ J]. IET Computer Vision, 2014, 8(2) : 118 - 130.

共引文献63

同被引文献12

引证文献1

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部