K. P. Sanal Kumar
Editorial Notes
NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the ICIA 2016 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.
ABSTRACT
Addressing the problem of recognizing egocentric actions is a challenging task. This recognition helps in assisting elderly people, disabled patients and so on. Here, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. In this research work, the recognition is done using the features like Histogram of Oriented Gradients (HOG) and Histogram of optical Flow (HOF). The extracted features are provided as input to the classifiers such as Support Vector Machine (SVM) and k Nearest Neighbor (kNN). The performance results showed that SVM gave better results than kNN classifier for both categories.
- Kenji Matsuo, Kentaro Yamada, Satoshi Ueno, Sei Naito, "An Attention-based Activity Recognition for Egocentric Video", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. Google Scholar
Digital Library
- H. Pirsiavash and D. Ramanan, "Detecting activities of daily living in first-person camera views", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012. Google ScholarDigital Library
- M. Cerf, J. Harel, W. Einhauser, and C. Koch. Predicting human gaze using low-level saliency combined with face detection. Advances in Neural Information Processing Systems (NIPS), Vol. 20, pp. 241--248, 2007. Google ScholarDigital Library
- L. Itti, N. Dhavale, F. Pighin, et al. Realistic avatar eye and head animation using a neurobiological model of visual attention. In SPIE 48th Annual International Symposium on Optical Science and Technology, Vol. 5200, pp. 64--78, 2003.Google Scholar
- J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. Advances in Neural Information Processing Systems (NIPS), Vol. 19, pp. 545--552, 2006. Google ScholarDigital Library
- C. Koch and S. Ullman. Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, Vol. 4, No. 4, pp. 219--227, 1985.Google Scholar
- L. Itti, C. Koch, and E. Neibur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 20, No. 11, pp. 1254--1259, 1998. Google ScholarDigital Library
- T. Avraham and M. Lindenbaum. Esaliency (extended saliency); Meaningful attention using stochastic image modeling. IEEE Transactions on Pattern Analysis and Machine intelligence (PAMI), Vol. 32, No. 4, pp. 693--708, 2010. Google ScholarDigital Library
- L. F. Coasta. Visual saliency and attention as random walks on complex networks. ArXiv Physics e-prints, 2006.Google Scholar
- W. Wang, Y. Wang, Q. Huang, and W. Gao. Measuring visual saliency by site entropy rate. In Computer Vision and Pattern Recognition (CVPR), pp. 2368--2375, IEEE, 2010.Google ScholarCross Ref
- T. Foulsham and G. Underwood. What can saliency models predict about eye movements? Spatial and sequential aspects of fixations during encoding and recognition. Journal of Vision, Vol. 8, No. 2;6, pp. 1--17, 2008.Google Scholar
- M. Cheng, G. Zhang, N. Mitra, X. Huang, and S. Hu. Global contrast based salient region detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. Google ScholarDigital Library
- K. Yamada, Y. Sugano, T. Okabe, Y. Sato, A. Sugimoto, and K. Hiraki. "Attention prediction in egocentric video using motion and visual saliency," in Proc. 5th Pacific-Rim Symposium on Image and Video Technology (PSIVT) 2011, vol. 1, pp. 277--288, Nov. 2011. Google ScholarDigital Library
- R. Messing, C. Pal, and H. Kautz, "Activity recognition using the velocity histories of tracked keypoints," in IEEE International Conference on Computer Vision, 2009.Google Scholar
- J. Lei, X. Ren, and D. Fox, "Fine-grained kitchen activity recognition using RGB-D," in ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2012. Google ScholarDigital Library
- M. Rohrbach, S. Amin, M. Andriluka, and B. Schiele, "A database for fine grained activity detection of cooking activities," in IEEE Conference on Computer Vision and Pattern Recognition, 2012. Google ScholarDigital Library
- H. Pirsiavash and D. Ramanan, "Detecting activities of daily living in first-person camera views," in IEEE Conference on Computer Vision and Pattern Recognition, 2012. Google ScholarDigital Library
- K. Ogaki, K. M. Kitani, Y. Sugano, and Y. Sato, "Coupling eye-motion and ego-motion features for first-person activity recognition," in CVPR Workshop on Egocentric Vision, 2012.Google Scholar
- E. Taralova, F. De la Torre, and M. Hebert, "Source constrained clustering," in IEEE International Conference on Computer Vision, 2011. Google ScholarDigital Library
- A. Fathi, Y. Li, and J. M. Rehg, "Learning to recognize daily actions using gaze," in European Conference on Computer Vision, 2012. Google ScholarDigital Library
- A. Fathi, A. Farhadi, and J. M. Rehg, "Understanding egocentric activities," in IEEE International Conference on Computer Vision, 2011. Google ScholarDigital Library
- M. S. Ryoo and L. Matthies, "First-person activity recognition: What are they doing to me?" in IEEE Conference on Computer Vision and Pattern Recognition, 2013. Google ScholarDigital Library
- Y. Poleg, C. Arora, and S. Peleg, "Temporal segmentation of egocentric videos," in IEEE Conference on Computer Vision and Pattern Recognition, 2014. Google ScholarDigital Library
- Kishor B. Bhangale and R. U. Shekokar, "Human Body Detection in static Images Using HOG & Piecewise Linear SVM", International Journal of Innovative Research & Development, vol. 3 no. 6, June 2014.Google Scholar
- Sri Devi Thota, Kanaka Sunanda Vemulapalli, Kartheek Chintalapati, Phanindra Sai Srinivas Gudipudi, "Comparison Between The Optical Flow Computational Techniques", International Journal of Engineering Trends and Technology, vol 4. No. 10, October 2013.Google Scholar
- M.Suresha, N.A. Shilpa and B.Soumya, "Apples Grading based on SVM Classifier", in National Conference on Advanced Computing and Communications, April 2012.Google Scholar
- OpenCV-Introduction to Support Vector Machines. http://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html Accessed: 2016-07-22.Google Scholar
- Wanpracha Art Chaovalitwongse, Ya-Ju Fan, Rajesh C. Sachdeo. On the Time Series K-Nearest Neighbor Classification of Abnormal Brain Activity. In IEEE Transactions on Systems, Man, and Cybernetics -- Part A: Systems and Humans, vol. 37, no. 6 November 2007. Google ScholarDigital Library
Recommendations
Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier
Selection and implementation of a face descriptor that is both discriminative and computationally efficient is crucial. Local Binary Patterns LBP and Histogram of Oriented Gradients HOG have been proven effective for face recognition. LBPs are fast to ...
One-shot-learning gesture recognition using HOG-HOF features
The purpose of this paper is to describe one-shot-learning gesture recognition systems developed on the ChaLearn Gesture Dataset (ChaLearn). We use RGB and depth images and combine appearance (Histograms of Oriented Gradients) and motion descriptors (...
Event recognition in egocentric videos using a novel trajectory based feature
ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image ProcessingThis paper proposes an approach for event recognition in Egocentric videos using dense trajectories over Gradient Flow - Space Time Interest Point (GF-STIP) feature. We focus on recognizing events of diverse categories (including indoor and outdoor ...
Comments