M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
A Lattice-Based MRF Model for Dynamic Near-Regular Texture Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
International Journal of Computer Vision
Deformed Lattice Detection in Real-World Images Using Mean-Shift Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusion reasoning for tracking multiple people
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
We propose a new dynamic Markov random field (DMRF) model to track a heavily occluded object. The DMRF model is a bidirectional graph which consists of three random variables: hidden, observation, and validity. It temporally prunes invalid nodes and links edges among valid nodes by verifying validities of all nodes. In order to apply the proposed DMRF model to the object tracking framework, we use an image block lattice model exactly correspond to nodes and edges in the DMRF model and utilize the mean-shift belief propagation (MSBP). The proposed object tracking method using the DMRF surprisingly tracks a heavily occluded object even if the occluded region is more than 70~80%. Experimental results show that the proposed tracking method gives good tracking performance even on various tracking image sequences(ex. human and face) with heavy occlusion.