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基于退火粒子群优化的单目视频人体姿态分析方法

李毅 孙正兴 陈松乐 李骞

李毅, 孙正兴, 陈松乐, 李骞. 基于退火粒子群优化的单目视频人体姿态分析方法. 自动化学报, 2012, 38(5): 732-741. doi: 10.3724/SP.J.1004.2012.00732
引用本文: 李毅, 孙正兴, 陈松乐, 李骞. 基于退火粒子群优化的单目视频人体姿态分析方法. 自动化学报, 2012, 38(5): 732-741. doi: 10.3724/SP.J.1004.2012.00732
LI Yi, SUN Zheng-Xing, CHEN Song-Le, LI Qian. 3D Human Pose Analysis from Monocular Video by Simulated Annealed Particle Swarm Optimization. ACTA AUTOMATICA SINICA, 2012, 38(5): 732-741. doi: 10.3724/SP.J.1004.2012.00732
Citation: LI Yi, SUN Zheng-Xing, CHEN Song-Le, LI Qian. 3D Human Pose Analysis from Monocular Video by Simulated Annealed Particle Swarm Optimization. ACTA AUTOMATICA SINICA, 2012, 38(5): 732-741. doi: 10.3724/SP.J.1004.2012.00732

基于退火粒子群优化的单目视频人体姿态分析方法

doi: 10.3724/SP.J.1004.2012.00732
详细信息
    通讯作者:

    孙正兴, 南京大学计算机科学与技术系教授. 主要研究方向为多媒体计算与计算机视觉.

3D Human Pose Analysis from Monocular Video by Simulated Annealed Particle Swarm Optimization

  • 摘要: 提出一种基于退火粒子群优化(Simulated annealing particle swarm optimism, SAPSO)的单目视频人体姿态分析方法. 该方法具有以下特点: 首先, 利用运动捕获数据采用主成分分析方法(Principle component analysis, PCA)得到更能反映人体运动本质的姿态紧致空间, 并在此低维空间中进行姿态分析, 提高了姿态分析的准确性和效率; 其次, 将粒子群优化应用到姿态分析中, 并提出退火粒子群优化姿态分析方法, 该方法具有良好的收敛性和全局最优能力; 再次, 基于退火粒子群优化姿态分析方法, 实现了基于单目视频的人体姿态估计和跟踪. 实验结果表明, 本文方法不仅具有良好的计算效率, 同时具有良好的收敛性和全局搜索能力, 能准确分析单目视频中的人体姿态.
  • [1] Sminchisescu C. 3D human motion analysis in monocular video: techniques and challenges. In: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (AVSS'06). Washington D.C., USA: IEEE, 2006. 76[2] Agarwal A, Triggs B. Recovering 3D human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(1): 44-58[3] Howe N R. Silhouette lookup for monocular 3D pose tracking. Image and Vision Computing, 2007, 25(3): 331-341[4] Moeslund T B, Hilton A, Krüger V. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding, 2006, 104(2-3): 90-126[5] Ronald P. Vision-based human motion analysis: an overview. Computer Vision and Image Understanding, 2007, 108(1-2): 4-18[6] Urtasun R, Fleet D J, Fua P. Monocular 3D tracking of the golf swing. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005. 932-938[7] Zhao X, Liu Y C. Generative tracking of 3D human motion by hierarchical annealed genetic algorithm. Pattern Recognition, 2008, 41(8): 2470-2483[8] Sminchisescu C, Jepson A. Generative modeling for continuous non-linearly embedded visual inference. In: Proceedings of the 21st International Conference on Machine Learning. New York, NY: ACM, 2004. 759-766[9] Wang Q, Xu G Y, Ai H Z. Learning object intrinsic structure for robust visual tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, USA: IEEE, 2003. 227-233[10] Urtasun R, Fleet D J, Hertzmann A, Fua P. Priors for people tracking from small training sets. In: Proceedings of the 10th IEEE International Conference on Computer Vision. Washington D.C., USA: IEEE Computer Society, 2005. 403-410[11] Wachter S, Nagel H H. Tracking persons in monocular image sequences. Computer Vision and Image Understanding, 1999, 74(3): 174-192[12] Gavrila D M, Davis L S. Tracking of humans in action: a 3D model-based approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 1996. 73-80[13] Deutscher J, Blake A, Reid I. Articulated body motion capture by annealed particle filtering. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Hilton Head, SC, USA: IEEE, 2000. 126-133[14] Sigal L, Balan A O, Black M J. HumanEva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. International Journal of Computer Vision, 2010, 87(1): 4-27[15] Peursum P, Venkatesh S, West G. A study on smoothing for particle-filtered 3D human body tracking. International Journal of Computer Vision, 2010, 87(1-2): 53-74[16] Daubney B, Xie X H. Tracking 3D human pose with large root node uncertainty. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO, USA: IEEE, 2011. 1321-1328[17] Wang X Y, Wan W G, Zhang X Q. Annealed particle filter based on particle swarm optimization for articulated three-dimensional human motion tracking. Optical Engineering, 2010, 49(1): 017204-11[18] Krzeszowski T, Kwolek B, Wojciechowski K. Articulated body motion tracking by combined particle swarm optimization and particle filtering. In: Proceedings of the 2010 International Conference on Computer Vision and Graphics: Part I. Warsaw, Poland: LNCS, 2010. 147-154[19] Vijay J, Emanuele T, Spela I. Markerless human articulated tracking using hierarchical particle swarm optimisation. Image and Vision Computing, 2010, 28(11): 1530-1547
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出版历程
  • 收稿日期:  2011-09-09
  • 修回日期:  2012-01-05
  • 刊出日期:  2012-05-20

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