A Review on Classification Techniques for Human Activity Recognition

Authors

  • Sonali Research Scholar, CE Department, YCOE, Punjabi University, Patiala, India
  • Ashok Kumar Bathla Assistant Professor, CE Department, YCOE, Punjabi University, Patiala, India

DOI:

https://doi.org/10.53555/nnbma.v1i2.131

Keywords:

action recognition, classification, support vector machine, nearest neighbor, bag of visual words

Abstract

Recognizing human actions from video sequences has many important applications like video surveillance, patient monitoring, human computer interaction, dance choreography analysis, analysis of sports events and entertainment environments. It involves processing the video into frames firstly and finding out the interest points, then extracting the features and lastly specifying and labeling the videos following an appropriate classifying approach like Support Vector Machine, bag of words or nearest neighbor. This paper provides a detailed overview of various state-of-the-art research papers on human activity recognition using different types of classifiers. We surveyed various challenges exhibited by computer vision researchers like the problem of occlusion, 2D/3D pose estimation, variations in viewpoints, human body modeling especially of a person who is paralyzed or injured. From this survey, we can make conclusion of various advantageous and disadvantageous facts about different classifiers used in the detection and classification task.

References

Aggarwal J. K., Ryoo M. S.,“ Human Activity Analysis: A Review”, Journal on ACM Computing Surveys (CSUR), 2011, Vol.43, No. 3, pp.1-47.

Brendel W., Todorovic S., “Activities as time series of human postures”, Proceedings of ECCV, Crete, 2010, Vol. 6312, pp.721-734.

Chua T. W., Leman K., Pham N. T., “ Human Action Recognition via Sum-Rule Fusion of Fuzzy K-Nearest Neighbor Classifiers”, IEEE International Conference on Fuzzy Systems (FUZZ)”, Taiwan, 2011, pp.484-489.

Gorelick L., Blank M., Shechtman E., Irani M.,Basri R., “Actions as Space-Time Shapes”, IEEE Tenth International Conference on Computer Vision

(ICCV), Beijing, 2005, Vol.2, pp.1395-1402.

Kaghyan S., Sarukhanyan H., “Activity Recognition using k-nearest neighbor algorithm on smartphone with tri-axial accelerometer”, International Journal on Information Models and Analyses", 2012, Vol 1, pp. 146-156.

Ke S. R. , Thuc H. L. U., Lee Y. J., Hwang J. N. , Yoo J. H. , Choi K. H., “A Review on Video-Based Human Activity Recognition”, 2013, Vol. 2, pp.88–131.

Maji S., Bourdev L., Malik J., “Action Recognition from a Distributed Representation of Pose and Appearance”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, 2011, pp.3177-3184.

Paul M., Haque S. M. E., Chakraborty S., “Human detection in surveillance videos and its applications -a review”, Springer EURASIP Journal on Advances in Signal Processing, 2013, pp.1-16.

Ramanathan M., Yau W.Y., Teoh E.k., “Human Action Recognition with video data: Research and Evaluation Challenges ”, IEEE Transaction on

Human-Machine Systems, 2014, vol.44, no.5, pp.650-663.

Rani T.J., Priyadharsini S.S., “Region of Interest Tracking In Video Sequences”, International Journal of Computer Applications ,2010, Vol. 3, No.7, pp. 32-36.

Ryoo M.S., “Human Activity Prediction: Early Recognition of Ongoing Activities from Streaming Videos”, IEEE International Conference on Computer Vision, 2011, pp.-1036-1043.

Sadanand S., Corso J. J., “Action Bank: A HighLevel Representation of Activity in Video”, IEEE Conference on Computer Vision and Pattern

Recognition (CVPR), Providence, 2012, pp.1234 –1241.

Schuldt C., Laptev I., Caputo B., “Recognizing Human Actions: A Local SVM Approach”, IEEE International Conference on Pattern Recognition,

, Vol 3, pp. 32-36

Vemulapalli R., Arrate F., Chellappa R., “Human Action Recognition by Representing 3D Skeltons as points in a lie group ”, IEEE Conference on computer vision and

Wang H., Yuan C., Hu W., Ling H., Yang W., Sun C., “Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection”, IEEE transactions on image processing, 2014, Vol. 23, No. 2, pp.570-581.

Yang M., Lv F., Xu W., Yu K., Gong Y., “Human Action Detection by Boosting Efficient Motion Features” IEEE 12th International Conference on

Computer Vision Workshops (ICCV), Kyoto, 2009, pp. 522-529.

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Published

28-02-2015

How to Cite

Sonali, & Bathla, A. K. (2015). A Review on Classification Techniques for Human Activity Recognition. Journal of Advance Research in Business, Management and Accounting (ISSN: 2456-3544), 1(2), 01-05. https://doi.org/10.53555/nnbma.v1i2.131