A Study on Visible to Infrared Action Recognition

Human action recognition is important in image and video processing with many applications. With the development of sensor technology, different cameras can be used for action acquisition, e.g., infrared cameras. Is it possible to adapt the visible light action recognizers to a new modality or domain? In this paper, we study the feasibility to adapt the action recognizer learned from visible light spectrum to infrared. A preliminary result is obtained on a large database based on an adaptive learning method, demonstrating the potential to perform cross-spectral action recognition.

[1]  Bir Bhanu,et al.  Human Activity Recognition in Thermal Infrared Imagery , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[2]  Rong Yan,et al.  Cross-domain video concept detection using adaptive svms , 2007, ACM Multimedia.

[3]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[4]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[5]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[6]  Ronen Basri,et al.  Actions as space-time shapes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[7]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[8]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[9]  Seong G. Kong,et al.  Recent advances in visual and infrared face recognition - a review , 2005, Comput. Vis. Image Underst..

[10]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

[11]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[12]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .