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.Abstract: In this paper we proposed a simulated annealing particle swarm optimism (SAPSO) based method for human pose estimation form monocular image sequences. First, we use principle component analysis (PCA) to learn the low-dimensional compact space of human pose, by which the aim of both reducing dimensionality and extracting the prior knowledge of human motion are achieved simultaneously. Pose is estimated on the compact subspace. In the optimizing step, we introduce particle swarm optimism to human pose estimation, and further, a SAPSO pose estimation method is proposed. And last we use SAPSO to estimate and track human pose in monocular videos separately. Experimental results demonstrate that the proposed method is more convergent and globally optimum, which can estimate and track human pose in monocular images effectively.
-
[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
点击查看大图
计量
- 文章访问数: 2426
- HTML全文浏览量: 35
- PDF下载量: 1093
- 被引次数: 0