Name
Abstract | ||
|---|---|---|
The aim of the work is to build self-growing based architectures to support visual surveillance and human-computer interaction systems. The objectives include: identifying and tracking persons or objects in the scene or the interpretation of user gestures for interaction with services, devices and systems implemented in the digital home. The system must address multiple vision tasks of various levels such as segmentation, representation or characterization, analysis and monitoring of the movement to allow the construction of a robust representation of their environment and interpret the elements of the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from acquisition devices at video frequency and offering results to higher level systems, monitors and take decisions in real time, and must accomplish a set of requirements such as: time constraints, high availability, robustness, high processing speed and re-configurability. Based on our previous work with neural models to represent objects, in particular the Growing Neural Gas (GNG) model and the study of the topology preservation as a function of the parameters election, it is proposed to extend the capabilities of this self-growing model to track objects and represent their motion in image sequences under temporal restrictions. These neural models have various interesting features such as: their ability to readjust to new input patterns without restarting the learning process, adaptability to represent deformable objects and even objects that are divided in different parts or the intrinsic resolution of the problem of matching features for the sequence analysis and monitoring of the movement. It is proposed to build an architecture based on the GNG that has been called GNG-Seq to represent and analyze the motion in image sequences. Several experiments are presented that demonstrate the validity of the architecture to solve problems of target tracking, motion analysis or human-computer interaction. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.asoc.2011.02.007 | Appl. Soft Comput. |
Keywords | Field | DocType |
image sequence,complex environment,human-computer interaction application,surveillance systems,motion analysis,human–computer interaction,growing neural gas,high processing speed,global system,topology preservation,self-growing models,sequence analysis,human-computer interaction system,neural model,human-computer interaction,high availability,self-growing model,human computer interaction | Adaptability,Computer vision,Architecture,Gesture,Segmentation,Computer science,Robustness (computer science),Artificial intelligence,Motion analysis,High availability,Machine learning,Neural gas | Journal |
Volume | Issue | ISSN |
11 | 7 | Applied Soft Computing Journal |
Citations | PageRank | References |
14 | 0.57 | 50 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José García Rodríguez | 1 | 192 | 29.10 |
| Juan Manuel García-Chamizo | 2 | 72 | 8.98 |