@InCollection{CU2010-JST,
  author = 	 {Shuichi Nishio and Norihiro Hagita and Takehiro Miyashita and Takayuki Kanda and Noriaki Mitsunaga and Masahiro Shiomi and Tatsuya Yamazaki},
  title = 	 {Sensor Network for Structuring People and Environmental Information},
  pages = 	 {367--378},
  editor = 	 {Vedran Kordic},
  booktitle = 	 {Cutting Edge Robotics 2010},
  year = 	 2010,
  month = 	 oct,
  chapter = 	 23,
  publisher = {In-Teh},
  address = 	 {Vukovar, Croatia},
  url = {http://www.intechopen.com/books/cutting-edge-robotics-2010/sensor-network-for-structuring-people-and-environmental-information},
  pdf = {http://www.intechopen.com/download/pdf/12212},
}

@Article{Matsumoto2010,
  author = 	 {Yusuke Matsumoto and Toshikazu Wada and Shuichi Nishio and Takehiro Miyashita and Norihiro Hagita},
  title = 	 {Scalable and Robust Multi-People Head Tracking by Combining Distributed Multiple Sensors},
  journal = 	 {Journal of Intelligent Service Robotics},
  year = 	 2010,
  volume = 	 3,
  number = 	 1,
  pages = 	 {29--36},
   url = {http://dx.doi.org/10.1007/s11370-009-0056-5},
   doi = {10.1007/s11370-009-0056-5},
   abstract = {In this paper, we present a robust 3D human-head tracking method. 3D head positions are essential for robots interacting with people. Natural interaction behaviors such as making eye contacts require head positions. Past researches with laser range finder (LRF) have been successful in tracking 2D human position with high accuracy in real time. However, LRF trackers cannot track multiple 3D head positions. On the other hand, trackers with multi-viewpoint images can obtain 3D head position. However, vision-based trackers generally lack robustness and scalability, especially in open environments where lightening conditions vary by time. To achieve 3D robust real-time tracking, here we propose a new method that combines LRF tracker and multi-camera tracker. We combine the results from trackers using the LRF results as maintenance information toward multi-camera tracker. Through an experiment in a real environment, we show that our method outperforms toward existing methods, both in its robustness and scalability.},
}

@InProceedings{ICPR2010,
  author    = {Shuichi Nishio and
               Hiromi Okamoto and
               Noboru Babaguchi},
  title     = {Hierarchical Anomality Detection Based on Situation},
  booktitle     = {Proc. 20th International Conference on Pattern Recognition (ICPR2010)},
  year      = 2010,
  pages     = {1108-1111},
  address = 	 {Istanbul, Turkey},
  doi = {10.1109/ICPR.2010.277  },
  url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5595871},
  abstract = {In this paper, we propose a novel anomality detection method based on external situational information and hierarchical analysis of behaviors. Past studies model normal behaviors to detect anomality as outliers. However, normal behaviors tend to differ by situations. Our method combines a set of simple classifiers with pedestrian trajectories as inputs. As mere path information is not sufficient for detecting anomality, trajectories are first decomposed into hierarchical features of different abstract levels and then applied to appropriate classifiers corresponding to the situation it belongs to. Effects of the methods are tested using real environment data.},
}

@InProceedings{URAI2008-JST,
  author = 	 {Yusuke Matsumoto and Toshikazu Wada and Shuichi Nishio and Takehiro Miyashita and Norihiro Hagita},
  title = 	 {Scalable Multi-People Head Tracking for Robotic Services Combining Multiple Sensors},
  booktitle = {Proc. The 5th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI2008)},
  pages = 	 {581-586},
  year = 	 2008,
  address = 	 {Seoul, Korea},
  note = {Outstanding Paper Award},
}

@InProceedings{nishio-KJNRS2006,
  author = "Shuichi Nishio and Noboru Babaguchi and Norihiro Hagita",
  title = {Personal identification in unconscious sensing by video and GPS positioning},
  booktitle = {Proc. 2nd Korea-Japan Joint Symposium on Network Robot System (KJ-NRS2006)},
  address = {Jeju, Korea},
  month = jun,
  year = 2006,
}