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Conference Paper
Robotic Person-Tracking with Modified Multiple Instance Learning
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- Authors
- Woo-han Yun, Young-Jo Cho, Dohyung Kim, Jaeyeon Lee, Hosub Yoon, Jaehong Kim
- Issue Date
- 2013-08
- Citation
- International Symposium on Robot and Human Interactive Communication (RO-MAN) 2013, pp.198-203
- Language
- English
- Type
- Conference Paper
- Abstract
- Robotic person-following is an essential component for natural human robot interaction. To follow a person, the robot should track the target person robustly and in real time. Object tracking algorithms in the computer vision field typically require abundant features and heavy computing power, and thus cannot be directly applied to person-following robots due to the problems arising in practical robotic environments. This paper proposes a robotic person-tracking algorithm based on modified multiple instance learning. In order to resolve the problems raised by the rearward view of the target person, the tracker is modified to be guided by color histogram back-projection. Additionally, the search area model is modified from circle to ellipse and the number of features is reduced so that the tracker should adapt the robotic environment in real-time. The algorithm is validated through system integration and experiments. © 2013 IEEE.
- KSP Keywords
- Back projection(BP), Color histogram, Computer Vision(CV), Computing power, Human robot interaction(HRI), Multiple instance learning(MIL), Object Tracking, Person tracking, Real-time, Tracking algorithm, area model