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가중치가 적용된 DTW를 이용한 다중 센서 기반의 동작인식 = Multi-sensor based Gesture Recognition using Weighted DTW

최효림 2018년

논문상세정보
인용/피인용
' 가중치가 적용된 DTW를 이용한 다중 센서 기반의 동작인식 = Multi-sensor based Gesture Recognition using Weighted DTW' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • dtw
  • 다중 센서
  • 데이터퓨전
  • 동작 인식
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
82 0

0.0%

' 가중치가 적용된 DTW를 이용한 다중 센서 기반의 동작인식 = Multi-sensor based Gesture Recognition using Weighted DTW' 의 참고문헌

  • https://kr.mathworks.com/help/stats/hidden-markov-models-hmm.html
  • http://www.microsoft.com/en-us/kinectforwindows/
  • Z. Cai, J. Han, L. Liu, and L. Shao, "RGB-D datasets using microsoft kinect or similar sensors: a survey," Multimedia Tools and Applications, vol. 76, no. 3, pp. 4313-4355, 2017.
  • Y. Yin and R. Davis, "Gesture spotting and recognition using salience detection and concatenated hidden markov models," in Proceedings of the 15th ACM on International conference on multimodal interaction , 2013, pp. 489-494.
  • Y. Hou, Z. Li, P. Wang, and W. Li, "Skeleton optical spectra based action recognition using convolutional neural networks," IEEE Transactions on Circuits and Systems for Video Technology, 2016.
  • X. Yin, D. Guo, and M. Xie, "Hand image segmentation using color and RCE neural network," Robotics and Autonomous Systems, vol. 34, no. 4, pp. 235-250, 2001.
  • W. Lu, Z. Tong, and J. Chu, "Dynamic hand gesture recognition with leap motion controller," IEEE Signal Processing Letters, vol. 23, no. 9, pp. 1188-1192, 2016.
  • V. I. Pavlovic, R. Sharma, and T. S. Huang, "Visual interpretation of hand gestures for human-computer interaction: A review," IEEE Transactions on pattern analysis and machine intelligence, vol. 19, no. 7, pp. 677-695, 1997.
  • V. Ganapathi, C. Plagemann, D. Koller, and S. Thrun, "Real time motion capture using a single time-of-flight camera," in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on , 2010, pp. 755-762.
  • V. Bloom, D. Makris, and V. Argyriou, "G3d: A gaming action dataset and real time action recognition evaluation framework," in Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on , 2012, pp. 7-12.
  • V. Argyriou, M. Petrou, and S. Barsky, "Photometric stereo with an arbitrary number of illuminants," Computer Vision and Image Understanding, vol. 114, no. 8, pp. 887-900, 2010.
  • S. Mitra and T. Acharya, "Gesture recognition: A survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 3, pp. 311-324, 2007.
  • S. Li, P. N. Pathirana, and T. Caelli, "Multi-kinect skeleton fusion for physical rehabilitation monitoring," in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE , 2014, pp. 5060-5063.
  • S. Jiang, Y. Cao, S. Iyengar, P. Kuryloski, R. Jafari, Y. Xue, R. Bajcsy, and S. Wicker, "CareNet: an integrated wireless sensor networking environment for remote healthcare," in Proceedings of the ICST 3rd international conference on Body area networks , 2008, p. 9: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).
  • S. Fothergill, H. Mentis, P. Kohli, and S. Nowozin, "Instructing people for training gestural interactive systems," in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 2012, pp. 1737-1746.
  • S. De Silva, M. Barlow, and A. Easton, "An evaluation of DTW approaches for whole-of-body gesture recognition," in Proceedings of the 28th International BCS Human Computer Interaction Conference on HCI 2014-Sand, Sea and Sky-Holiday HCI , 2014, pp. 11-21.
  • S. Das and P. N. Suganthan, "Differential evolution: A survey of the state-of-the-art," IEEE transactions on evolutionary computation, vol. 15, no. 1, pp. 4-31, 2011.
  • R. Sheikhpour, M. A. Sarram, S. Gharaghani, and M. A. Z. Chahooki, "A survey on semi-supervised feature selection methods," Pattern Recognition, vol. 64, pp. 141-158, 2017.
  • R. S. Yang et al. , "Multi-Kinect scene reconstruction: Calibration and depth inconsistencies," in Image and Vision Computing New Zealand (IVCNZ), 2013 28th International Conference of , 2013, pp. 47-52.
  • R. Poppe, "A survey on vision-based human action recognition," Image and vision computing, vol. 28, no. 6, pp. 976-990, 2010.
  • R. Gravina, P. Alinia, H. Ghasemzadeh, and G. Fortino, "Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges," Information Fusion, vol. 35, pp. 68-80, 2017.
  • Q. Chun and E. Zhang, "Human action recognition based on improved motion history image and deep convolutional neural networks," in Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017 10th International Congress on , 2017, pp. 1-5.
  • P. Wang, Z. Li, Y. Hou, and W. Li, "Action recognition based on joint trajectory maps using convolutional neural networks," in Proceedings of the 2016 ACM on Multimedia Conference , 2016, pp. 102-106.
  • P. Wang, W. Li, Z. Gao, J. Zhang, C. Tang, and P. O. Ogunbona, "Action recognition from depth maps using deep convolutional neural networks," IEEE Transactions on Human-Machine Systems, vol. 46, no. 4, pp. 498-509, 2016.
  • P. Rocca, G. Oliveri, and A. Massa, "Differential evolution as applied to electromagnetics," IEEE Antennas and Propagation Magazine, vol. 53, no. 1, pp. 38-49, 2011.
  • N. Kern, B. Schiele, and A. Schmidt, "Multi-sensor activity context detection for wearable computing," in European Symposium on Ambient Intelligence , 2003, pp. 220-232.
  • N. Kamijoh, T. Inoue, C. M. Olsen, M. T. Raghunath, and C. Narayanaswami, "Energy trade-offs in the IBM wristwatch computer," in Wearable Computers, 2001. Proceedings. Fifth International Symposium on , 2001, pp. 133-140.
  • N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, vol. 1, pp. 886–893.
  • N. Barbour and G. Schmidt, "Inertial sensor technology trends," IEEE Sensors journal, vol. 1, no. 4, pp. 332-339, 2001.
  • M. Reyes, G. Dominguez, and S. Escalera, "Featureweighting in dynamic timewarping for gesture recognition in depth data," in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on , 2011, pp. 1182-1188.
  • M. Ramanathan, W.-Y. Yau, and E. K. Teoh, "Human action recognition with video data: research and evaluation challenges," IEEE Transactions on human-machine systems, vol. 44, no. 5, pp. 650-663, 2014.
  • M. A. Livingston, J. Sebastian, Z. Ai, and J. W. Decker, "Performance measurements for the Microsoft Kinect skeleton," in Virtual Reality Short Papers and Posters (VRW), 2012 IEEE , 2012, pp. 119-120.
  • K. Liu, C. Chen, R. Jafari, and N. Kehtarnavaz, "Fusion of inertial and depth sensor data for robust hand gesture recognition," IEEE Sensors Journal, vol. 14, no. 6, pp. 1898-1903, 2014.
  • K. Khoshelham and S. O. Elberink, "Accuracy and resolution of kinect depth data for indoor mapping applications," Sensors, vol. 12, no. 2, pp. 1437-1454, 2012.
  • K. Barczewska and A. Drozd, "Comparison of methods for hand gesture recognition based on Dynamic Time Warping algorithm," in Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on , 2013, pp. 207-210.
  • K. Altun and B. Barshan, "Human activity recognition using inertial/magnetic sensor units," in International Workshop on Human Behavior Understanding , 2010, pp. 38-51.
  • J. Suarez and R. R. Murphy, "Hand gesture recognition with depth images: A review," in Ro-man, 2012 IEEE , 2012, pp. 411-417.
  • J. K. Aggarwal and M. S. Ryoo, "Human activity analysis: A review," ACM Computing Surveys (CSUR), vol. 43, no. 3, p. 16, 2011.
  • J. K. Aggarwal and L. Xia, "Human activity recognition from 3d data: A review," Pattern Recognition Letters, vol. 48, pp. 70-80, 2014.
  • J. C. Coelho and F. J. Verbeek, "Pointing task evaluation of leap motion controller in 3D virtual environment," Creating the Difference, vol. 78, pp. 78-85, 2014.
  • I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of machine learning research, vol. 3, no. Mar, pp. 1157–1182, 2003.
  • H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization for spoken word recognition," in Readings in speech recognition : Elsevier, 1990, pp. 159-165.
  • H. P. Jain, A. Subramanian, S. Das, and A. Mittal, "Real-time upper-body human pose estimation using a depth camera," in International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications , 2011, pp. 227-238.
  • H. K. Lee and J. H. Kim, "An HMM-based threshold model approach for gesture recognition," IEEE Transactions on pattern analysis and machine intelligence, vol. 21, no. 10, pp. 961-973, 1999.
  • H. Jin, Q. Chen, Z. Chen, Y. Hu, and J. Zhang, "Multi-LeapMotion sensor based demonstration for robotic refine tabletop object manipulation task," CAAI Transactions on Intelligence Technology, vol. 1, no. 1, pp. 104-113, 2016.
  • F. Zhou, F. De la Torre, and J. K. Hodgins, "Aligned cluster analysis for temporal segmentation of human motion," in Automatic Face & Gesture Recognition, 2008. FG'08. 8th IEEE International Conference on , 2008, pp. 1-7.
  • E. N. Mortensen, H. Deng, and L. Shapiro, "A SIFT descriptor with global context," in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on , 2005, vol. 1, pp. 184-190.
  • E. M. Taranta II, T. K. Simons, R. Sukthankar, and J. J. Laviola Jr, "Exploring the Benefits of Context in 3D Gesture Recognition for Game-Based Virtual Environments," ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 5, no. 1, p. 1, 2015.
  • E. J. Keogh and M. J. Pazzani, "Derivative dynamic time warping," in Proceedings of the 2001 SIAM International Conference on Data Mining , 2001, pp. 1-11.
  • E. H. Spriggs, F. De La Torre, and M. Hebert, "Temporal segmentation and activity classification from first-person sensing," in Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference On , 2009, pp. 17-24.
  • D.-D. Nguyen and H.-S. Le, "Kinect gesture recognition: Svm vs. rvm," in Knowledge and Systems Engineering (KSE), 2015 Seventh International Conference on , 2015, pp. 395-400.
  • D. Weinland, R. Ronfard, and E. Boyer, "A survey of vision-based methods for action representation, segmentation and recognition," Computer vision and image understanding, vol. 115, no. 2, pp. 224-241, 2011.
  • D. Chai and K. N. Ngan, "Locating facial region of a head-and-shoulders color image," in Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on , 1998, pp. 124-129.
  • D. Bachmann, F. Weichert, and G. Rinkenauer, "Evaluation of the leap motion controller as a new contact-free pointing device," Sensors, vol. 15, no. 1, pp. 214-233, 2014.
  • C. Sminchisescu, A. Kanaujia, and D. Metaxas, "Conditional models for contextual human motion recognition," Computer Vision and Image Understanding, vol. 104, no. 2-3, pp. 210-220, 2006.
  • C. Li, Y. Hou, P. Wang, and W. Li, "Joint distance maps based action recognition with convolutional neural networks," IEEE Signal Processing Letters, vol. 24, no. 5, pp. 624-628, 2017.
  • C. Chen, R. Jafari, and N. Kehtarnavaz, "Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor," in Image Processing (ICIP), 2015 IEEE International Conference on , 2015, pp. 168-172.
  • C. Chen, R. Jafari, and N. Kehtarnavaz, "A survey of depth and inertial sensor fusion for human action recognition," Multimedia Tools and Applications, vol. 76, no. 3, pp. 4405-4425, 2017.
  • C. Chen, K. Liu, R. Jafari, and N. Kehtarnavaz, "Home-based senior fitness test measurement system using collaborative inertial and depth sensors," in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE , 2014, pp. 4135-4138.
  • B. Sabata, F. Arman, and J. K. Aggarwal, "Segmentation of 3D range images using pyramidal data structures," in Computer Vision, 1990. Proceedings, Third International Conference on , 1990, pp. 662-666.
  • A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu, and P. Havinga, "Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey," in Architecture of computing systems (ARCS), 2010 23rd international conference on , 2010, pp. 1-10.