Abstract:
In human-robot interaction scenarios, the ability to identify a single object from multiple objects is an important task for service robots. Although there has been recent progress in this area, it remains difficult for autonomous vision systems to recognize objects in natural conditions. The service robot should detect a particular object according to the user’s demand. This paper describes a human robot interaction framework to detect a particular household object from multiple objects through text based interaction. Haar Cascade Classifiers is used to detect objects and developed a user friendly interface for human-system interaction. The propose framework use color, size, or position information to distinguish the user requested object in multi object scenarios. Evaluation results shows that the system is quite effective to detect the target household object from multiple objects in real time.
Description:
The ICIEV provides vibrant opportunities for researchers, industry practitioners and students to share their research experiences, research results, ideas, review of various aspects and practical development experiences on Informatics, Electronics, Computer Vision and related fields. Through various presentations from peer-reviewed accepted papers, Special Talks and networking - the ICIEV provides the avenue to share knowledge, make networks, and develop a community for the new researchers - based on the experiences of experts. The ICIEV will open doors for challenging research areas for future. The ICIEV welcomes you to be part of it - through offering Special Session, Tutorial, Workshop, Special Talk, Panel Discussion, and through submitting your research paper on and related arenas!
