Name
Abstract | ||
|---|---|---|
This paper presents a new methodology for improving the tracking of multiple targets in complex scenes. The new method, Motion Parameter Sharing, incorporates social motion information into tracking predictions. This is achieved by allowing a tracker to share motion estimates within groups of targets which have previously been moving in a coordinated fashion. The method is intuitive and, as well as aiding the prediction estimates, allows the implicit formation of ‘social groups’ of targets as a side effect of the process. The underlying reasoning and method are presented, as well as a description of how the method fits into the framework of a typical Bayesian tracking system. This is followed by some preliminary results which suggest the method is more accurate and robust than algorithms which do not incorporate the social information available in multiple target scenarios. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/AVSS.2007.4425312 | AVSS |
Keywords | Field | DocType |
motion estimation,side effect,tracking system | Social group,Computer vision,Computer science,Tracking system,Motion parameter,Artificial intelligence,Social effects,Social information,Motion estimation,Machine learning,Bayesian probability | Conference |
Citations | PageRank | References |
8 | 0.78 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew French | 1 | 31 | 4.37 |
| Asad Naeem | 2 | 15 | 1.60 |
| Ian L. Dryden | 3 | 38 | 5.68 |
| Tony P. Pridmore | 4 | 143 | 40.24 |