Name
Title | ||
|---|---|---|
Object tracking under low signal-to-noise-ratio with the instantaneous-possible-moving-position model |
Abstract | ||
|---|---|---|
Combing image processing technique and the probabilistic data association (PDA) motion model, we develop a novel framework to solve the problem of object tracking for non-electromechanical system with overwhelming noise background. The new model has two advantages: (1) By integrating the statistical motion model, the movement of object in many non-electromechanical systems could be more precisely simulated than existing ones. (2) Because of the adoption of a global search for optimal model parameters, the proposed model is better to track objects in high noise environment, comparing with other methods that rely on consecutive frames differentiating. We derive the expectation-maximization (EM) algorithm within the proposed model. Its usefulness is demonstrated with both synthesized data and image data set. Model Stability is introduced to quantify the usefulness of the model. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.sigpro.2012.11.018 | Signal Processing |
Keywords | Field | DocType |
object tracking,image data,instantaneous-possible-moving-position model,motion model,statistical motion model,non-electromechanical system,high noise environment,synthesized data,optimal model parameter,new model,probabilistic data association,low signal-to-noise-ratio,em algorithm,hidden markov models | Computer vision,Expectation–maximization algorithm,Computer science,Signal-to-noise ratio,Image processing,Data association,Video tracking,Artificial intelligence,Probabilistic logic,Hidden Markov model | Journal |
Volume | Issue | ISSN |
93 | 5 | 0165-1684 |
Citations | PageRank | References |
1 | 0.35 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng-Liang Wang | 1 | 16 | 2.06 |
| Xiaoming Huo | 2 | 157 | 24.83 |