G3di: A gaming interaction dataset with a real time detection and evaluation framework

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8925, Issue , 2015, Pages 698-712

  Bloom, Victoria ^a   Argyriou, Vasileios ^a   Makris, Dimitrios ^a  

^a KINGSTON UNIVERSITY   (United Kingdom)

Author keywords

Human interaction recognition; Interaction evaluation metric; Multimodal dataset; Multiplayer gaming

Indexed keywords

VIRTUAL REALITY;

DETECTION FRAMEWORK; EVALUATION FRAMEWORK; HUMAN INTERACTION RECOGNITION; INTERACTION EVALUATIONS; MULTI-MODAL DATASET; MULTI-PLAYER GAMING; REAL-TIME APPLICATION; REAL-TIME DETECTION;

COMPUTER VISION;

EID: 84925308820     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-16178-5_49     Document Type: Conference Paper

References (20)

1. Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: A review. ACM Computing Surveys 43(3), 16:1–16:43 (2011)
2. Aggarwal, J., Xia, L.: Human Activity Recognition From 3D Data: A Review. Pattern Recognition Letters (2014)
3. Bloom, V., Argyriou, V., Makris, D.: Dynamic feature selection for online action recognition. In: Salah, A.A., Hung, H., Aran, O., Gunes, H. (eds.) HBU 2013. LNCS, vol. 8212, pp. 64–76. Springer, Heidelberg (2013)
4. Bloom, V., Makris, D., Argyriou, V.: G3D: A gaming action dataset and real time action recognition evaluation framework. In: Workshop on Computer Vision for Computer Games, IEEE Conference on Computer Vision and Pattern Recognition (CVPR Workshops), pp. 7–12 (2012)
5. Butler, A., Izadi, S., Hilliges, O., Molyneaux, D., Hodges, S., Kim, D.: Shake’n’Sense: reducing interference for overlapping structured light depth cameras. In: Human Factors in Computing Systems, pp. 1933–1936 (2012)
6. Escalera, S., Baró, X., González, J., Bautista, M.A., Madadi, M., Reyes, M., Ponce, V., Escalante, H.J., Shotton, J., Guyon, I.: ChaLearn Looking at People Challenge 2014: Dataset and Results. In: ECCV Workshop (2014)
7. Fothergill, S., Mentis, H., Kohli, P., Nowozin, S.: Instructing people for training gestural interactive systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1737–1746 (2012)
8. Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm (1996)
9. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 29(12), 2247–2253 (2007)
10. Hu, T., Zhu, X., Guo, W., Su, K.: Efficient Interaction Recognition through Positive Action Representation. Mathematical Problems in Engineering 2013, 1–11 (2013)
11. Li, W., Way, O.M.: Action Recognition Based on A Bag of 3D Points. In: IEEE Conference on Computer Vision and Pattern RecognitionWorkshop (CVPRWorkshops), pp. 9–14 (2010)
12. Ni, B., Moulin, P.: RGBD-HuDaAct: A color-depth video database for human daily activity recognition. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1147–1153 (2011)
13. Nowozin, S., Shotton, J.: Action Points: A Representation for Low-latency Online Human Action Recognition. Tech. Rep. MSR-TR-2012-68, Microsoft Research Cambridge (2012)
14. Park, S., Aggarwal, J.: Event semantics in two-person interactions. In: IEEE Conference on International Conference on Pattern Recognition (ICPR), vol. 4, pp. 227–230 (2004)
15. Ryoo, M.S., Aggarwal, J.K.: UT-Interaction Dataset. In: ICPR contest on Semantic Description of Human Activities (SDHA) (2010)
16. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: IEEE Conference on International Conference on Pattern Recognition (ICPR), vol. 4, pp. 32–36 (2004)
17. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1297–1304 (2011)
18. Wang, J.: Mining actionlet ensemble for action recognition with depth cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1290–1297 (2012)
19. Yao, A., Gall, J., Fanelli, G., Gool, L.V.: Does human action recognition benefit from pose estimation? In: Proceedings of the British Machine Vision Conference, pp. 67.1–67.11 (2011)
20. Yun, K., Honorio, J., Chattopadhyay, D., Berg, T.L., Samaras, D., Brook, S.: Twoperson interaction detection using body-pose features and multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR Workshops), pp. 28–35 (2012)