@inproceedings{5470ae0fe8524152ab4a6b91f98e286c,
title = "Demo abstract: Kintense: A robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3D skeleton data",
abstract = "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.",
keywords = "Aggressive Actions, Kinect, Skeletal Tracking",
author = "Shahriar Nirjon and Chris Greenwood and Carlos Torres and Stefanie Zhou and Stankovic, {John A.} and Yoon, {Hee Jung} and Ra, {Ho Kyeong} and Can Basaran and Taejoon Park and Son, {Sang H.}",
year = "2013",
doi = "10.1145/2517351.2517396",
language = "English",
isbn = "9781450320276",
series = "SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery",
booktitle = "SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems",
note = "11th ACM Conference on Embedded Networked Sensor Systems, SenSys 2013 ; Conference date: 11-11-2013 Through 15-11-2013",
}