Topology-Constrained Layered Tracking with Latent Flow

被引:7
作者
Chang, Jason [1 ]
Fisher, John W., III [1 ]
机构
[1] MIT, CSAIL, Cambridge, MA 02139 USA
来源
2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) | 2013年
关键词
D O I
10.1109/ICCV.2013.27
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present an integrated probabilistic model for layered object tracking that combines dynamics on implicit shape representations, topological shape constraints, adaptive appearance models, and layered flow. The generative model combines the evolution of appearances and layer shapes with a Gaussian process flow and explicit layer ordering. Efficient MCMC sampling algorithms are developed to enable a particle filtering approach while reasoning about the distribution of object boundaries in video. We demonstrate the utility of the proposed tracking algorithm on a wide variety of video sources while achieving state-of-the-art results on a boundary-accurate tracking dataset.
引用
收藏
页码:161 / 168
页数:8
相关论文
共 33 条
[1]  
[Anonymous], ICCV
[2]  
[Anonymous], 2010, ECCV
[3]  
[Anonymous], 2006, CVPR
[4]  
[Anonymous], ICCV
[5]  
[Anonymous], IEEE WORKSH VIS MOT
[6]  
[Anonymous], ICCV
[7]  
[Anonymous], 2010, P BRIT MACH VIS C
[8]  
[Anonymous], ICIP
[9]  
[Anonymous], 2010, CVPR
[10]  
[Anonymous], IEEE T IMAGE P