Representing feature quantization approach using spatial-temporal relation for action recognition

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

Volumn 7143 LNCS, Issue , 2012, Pages 98-105

  Vishwakarma, Sarvesh ^a   Agrawal, Anupam ^a  

^a INDIAN INSTITUTE OF INFORMATION TECHNOLOGY   (India)

Author keywords

Action Recognition; Feature Quantization; Histogram; Spatio temporal Feature

Indexed keywords

ACTION RECOGNITION; ACTIVITY RECOGNITION; FEATURE QUANTIZATION; HISTOGRAM; INTEREST POINTS; QUANTIZATION APPROACH; SPATIAL TEMPORALS; SPATIO-TEMPORAL; VIDEO SURVEILLANCE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; EXPERIMENTS; IMAGE RECOGNITION; MOTION ESTIMATION; SECURITY SYSTEMS; VECTOR QUANTIZATION; VECTOR SPACES;

FEATURE EXTRACTION;

EID: 84856057380     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-27387-2_13     Document Type: Conference Paper

References (15)

1. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8 (2008)
2. Savarese, S., Delpozo, A., Niebles, J., Fei-Fei, L.: Spatial-temporal correlatons for unsupervised action classification. In: Proc. IEEE Workshop on Motion and Video Computing, pp. 1-8 (2008)
3. Zarka, N., Alhalah, Z., Deeb, R.: Real-Time Human Motion Detection and Tracking. In: 3rd International Conference on ICTTA, vol. 08, pp. 1-6 (2008)
4. Turaga, P., Chellappa, R., Subrahmaniam, V.S., Udrea, O.: Machine Recognition of Human Activities: A Survey. IEEE Transactions on Circuits and Systems for Video Technology 18 (11), 1473-1488 (2008)
5. Benezeth, Y., Jodoin, P.M., Emile, B., Laurent, H., Rosenberger, C.: Review and Evaluation of Commonly-Implemented Background Subtraction Algorithms. In: 19th IEEE International Conference on ICPR, pp. 1-4 (2008)
6. Liu, J., Yang, J., Zhang, Y.: Action Recognition by Multiple Features and Hypersphere Multi-class SVM. In: 20th IEEE International Conference on ICPR, pp. 3744-3747 (2010)
7. Bregonzio, M., Gong, S., Xiang, T.: Recognising Action as Clouds of Space-Time Interest Points. In: IEEE International Conference on CVPR, pp. 1948-1955 (2009)
8. Liu, J., Shah, M.: Learning human actions via information maximization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8 (2008)
9. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes. In: 10th IEEE International Conference on ICCV, vol. 02, pp. 1395-1402 (2005)
10. Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio-Temporal Features. In: 2nd Joint IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, vol. 02, pp. 65-72 (2005)
11. Shabani, A.H., Zelek, J.S., Clausi, D.A.: Human Action Recognition using Salient Opponent-Based Motion Features. In: 7th Canadian Conference on Computer and Robot Vision, pp. 362-369. IEEE Computer Society (2010)
12. Kovashka, A., Grauman, K.: Learning a Hierarchy of Discriminative Space-Time Neighborhood Features for Human Action Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2046-2053 (2010)
13. Yan, X., Luo, Y.: Making Full Use of Spatial-Temporal Interest Points: An Adaboost Aproach for Action Recognition. In: 17th IEEE International Conference on Image Processing, pp. 4677-4680 (2010)
14. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach (2004), http://www.nada.kth.se/cvap/actions/
15. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes (2005), http://www.wisdom.weizmann.ac.il/vision/ SpaceTimeActions.html