Human-object interaction recognition by learning the distances between the object and the skeleton joints

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015

Volumn 2015-January, Issue , 2015, Pages

  Meng, Meng ^a   Drira, Hassen ^a   Daoudi, Mohamed ^a   Boonaert, Jacques ^a  

^a ECOLE DES MINES DE DOUAI   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION RECOGNITION; AUTOMATIC APPROACHES; DEPTH SENSORS; HUMAN-OBJECT INTERACTION; OBJECT DISTANCE; POSE VARIATION; SKELETON JOINTS; STATE OF THE ART;

GESTURE RECOGNITION;

EID: 85073424263     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FG.2015.7284883     Document Type: Conference Paper

References (14)

1. G. Yu, Z. Liu, J. Yuan, Discriminative Orderlet Mining For Real-time Recognition of Human-Object Interaction, Asian Conference on Computer Vision (ACCV) 2014.
2. L. Breiman, Random forests. Machine Learning, vol. 45, pp 5-32; 2001.
3. B.Yao, F. Li, Grouplet: A Structured Image Representation for Recognizing Human and Object Interactions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
4. R. Vemulapalli, F. Arrate, R. Chellapan, Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group,IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
5. L. Xia, C. Chen, J.K. Aggarwal, View Invariant Human Action Recognition Using Histograms of 3D Joints, International Workshop on Human Activity Understanding from 3D Data in conjunction with 23th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 2012.
6. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake, Real-time human pose recognition in parts from single depth images, Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Colorado Springs, Colorado, USA (June 2011) 1-8.
7. C. Desai, D. Ramanan,C. Fowlkes, Discriminative models for static human-object interactions, International Journal of Computer Vision 95.1 (2011): 1-12.
8. J. Wang, Z. Liu, Y. Wu, J. Yuan, Mining actionlet ensemble for action recognition with depth cameras,IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
9. B.Yao, F. Li, Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34.9 (2012): 1691-1703.
10. G.Dian, G.Medioni,Dynamic Manifold Warping for View Invariant Action Recognition, IEEE International Conference on Computer Vision (ICCV), 2011.
11. W.Li, Z.Zhang, Z. Liu, Action Recognition Based on A Bag of 3D Points, IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), 2010.
12. O. Omar, Z. Liu, HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
13. R. Slama, H. Wannous, M. Daoudi, A. Srivastava, Accurate 3D action recognition using learning on the Grassmann manifold, Pattern Recognition 48.2 (2015): 556-567.
14. M. Devanne, H. Wannous, Stefano Berretti, Mohamed Daoudi, Alberto Del Bimbo, Space-Time Pose Representation for 3D Human Action Recognition, New Trends in Image Analysis and Processing–ICIAP 2013. Springer Berlin Heidelberg, 2013. 456-464.