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Demo abstract: Kintense: A robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3D skeleton data
- Demo abstract: Kintense: A robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3D skeleton data
- Nirjon, Shahriar; Greenwood, Chris; Torres, Carlos; Zhou Stefanie; Stankovic, John A.; Yoon, Hee Jung; Ra, Ho-Kyeong; Basaran, Can; Park, Taejoon; Son, Sang H.
- DGIST Authors
- Yoon, Hee Jung; Ra, Ho-Kyeong; Basaran, Can; Park, Taejoon; Son, Sang H.
- Issue Date
- 11th ACM Conference on Embedded Networked Sensor Systems, SenSys 2013
- Article Type
- Conference Paper
- Kintense is a robust, accurate, real-time, and evolving system for detecting aggressive actions such as hitting, kicking, pushing, and throwing from streaming 3D skeleton joint coordinates obtained from Kinect sensors. Kintense uses a combination of: (1) an array of supervised learners to recognize a predefined set of aggressive actions, (2) an unsupervised learner to discover new aggressive actions or refine existing actions, and (3) human feedback to reduce false alarms and to label potential aggressive actions. This abstract provides an overview of the design and implementation of Kintense and provides empirical evidence that Kintense is 11%-16% more accurate when compared to standard techniques such as dynamic time warping (DTW) and posture based gesture recognizers.
- Association for Computing Machinery
- Related Researcher
Son, Sang Hyuk
RTCPS(Real-Time Cyber-Physical Systems Research) Lab
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- ETC2. Conference Papers
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