.png){kind=icon}
Return
Visual Tracking Based on Dynamic Coupled Conditional Random Field Model
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2015.2488287.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a novel approach to visual tracking of moving objects based on the dynamic coupled conditional random field (DcCRF) model. The principal idea is to integrate a variety of relevant knowledge about object tracking into a unified dynamic probabilistic framework, which is called the DcCRF model in this paper. Under this framework, the proposed approach integrates spatiotemporal contextual information of motion and appearance, as well as the compatibility between the foreground label and object label. An approximate inference algorithm, i.e., loopy belief propagation, is adopted to conduct the inference. Meanwhile, the background model is adaptively updated to deal with gradual background changes. Experimental results show that the proposed approach can accurately track moving objects (with or without occlusions) in monocular video sequences and outperforms some state-of-the-art methods in tracking and segmentation accuracy.
Keywords:
Coupled conditional random field
dynamic models
visual tracking
region-level tracking
spatiotemporal context
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.4
Papers:
9.5K
Citations:
6.3W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveResearch on the consumption mode of green electricity in China-Based on theory of reasoned action
OAAI
OAAIShareSaveShareSave
ShareSaveShareSaveShareSave
View more