Human Activity Recognition on Real Time and Offline Dataset
AbstractHuman activity recognition is an important area of research in the field of computer vision due to its extensive applications like security surveillance, content based video retrieval and annotation, human computer interaction, human fall detection, video summarization, robotics, etc. The surveillance system deals with the monitoring and analysing the human behaviour and activities. The main aim of the smart surveillance system is to recognize anomalous behaviour in given scene and provide real time intimation to relevant person. We have designed and tested SmartSurveillance System for College Corridor Scene (3S2CS). The system recognises the anomalous behaviour and an intimation is provided in the form of Firebase Cloud Messaging (FCM) alert on the android mobile phone to the authorised user. This paper mainly discuss the methodologies used for the human action recognition. The basic step is to provide video as an input. These videos are further divided into number of frames. The videos are used for training and for each video, Scale Invariant Feature Transform (SIFT) is applied for extracting features and developing feature vectors. The actions are classified using K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifier. Two standard offline datasets considered for testing are Weizmann and UTD-MHAD. For real time scenario we have created dataset in college campus called as College Corridor dataset. It contains student activities like falling, fighting, walking, running, sitting and other general actions. If falling or fighting action is detected, the notification is sent to the authorized user who has installed ActionDetector android application and registered a device for the same. In future, the system can be implemented for other real time applications using parallel programming for the fast processing of human action recognition.
Aggarwal, Jake K., and Michael S. Ryoo. "Human activity analysis: A review." ACM Computing Surveys (CSUR) Vol 43, no. 3, pp. 1-16, 2011, DOI:10.1145/1922649.1922653.
Kale, Geetanjali Vinayak, and Varsha Hemant Patil. "A study of vision based human motion recognition and analysis." International Journal of Ambient Computing and Intelligence (IJACI) 7, no. 2, pp 75-92, 2016.DOI: 10.4018/IJACI.2016070104.
Tran, Du, and Alexander Sorokin. "Human activity recognition with metric learning." In European conference on computer vision, pp. 548-561, Springer, Berlin, Heidelberg, 2008, DOI: 10.1007/978-3-540-88682-2_42.
Arikan, Okan, David A. Forsyth, and James F. O'Brien. "Motion synthesis from annotations." In ACM Transactions on Graphics (TOG), vol. 22, no. 3, pp. 402-408, 2003 ACM.
Zhang, Huiquan, and Osamu Yoshie. "Improving human activity recognition using subspace clustering." In Machine Learning and Cybernetics (ICMLC), 2012 International Conference on, vol. 3, pp. 1058-1063, 2012, IEEE.
Kailing, Karin, Hans-Peter Kriegel, and Peer Kröger. "Density-connected subspace clustering for high-dimensional data." In Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 246-256, 2004, Society for Industrial and Applied Mathematics.
Belongie, Serge, Jitendra Malik, and Jan Puzicha. Shape matching and object recognition using shape contexts. California Univ San Diego La Jolla Dept Of Computer Science And Engineering, Proceedings of the IEEE International Conference on Computer Vision, Vancouver, Canada, pp. 454–461, 2002.
Lowe, David G. "Distinctive image features from scale-invariant keypoints." International journal of computer vol. 60, no. 2, pp. 91–110, 2004.
Niebles, Juan Carlos, and Li Fei-Fei. "A hierarchical model of shape and appearance for human action classification." In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pp. 1-8, 2007.
Palaniappan, Adithyan, R. Bhargavi, and V. Vaidehi. "Abnormal human activity recognition using SVM based approach." In Recent Trends In Information Technology (ICRTIT), 2012 International Conference, pp. 97-102, 2012, ISBN: 978-1-4673-1601-9/12/$31.00.
Iqbal, JL Mazher, J. Lavanya, and S. Arun. "Abnormal Human Activity Recognition using Scale Invariant Feature Transform." International Journal of Current Engineering and Technology 5, vol. no. 6, pp. 3748-3751, 2015.
Laaksonen, Jorma, and Erkki Oja. "Classification with learning k-nearest neighbors." In Neural Networks, 1996., IEEE International Conference on, vol. 3, pp. 1480-1483. IEEE, 1996.
Babu, U. Ravi, Y. Venkateswarlu, and Aneel Kumar Chintha. "Handwritten digit recognition using K-nearest neighbour classifier." In Computing and Communication Technologies (WCCCT), 2014 World Congress on, pp. 60-65. IEEE, 2014.
Ann, Ong Chin, and Lau Bee Theng. "Human activity recognition: a review." In Control System, Computing and Engineering (ICCSCE), 2014 IEEE International Conference on, pp. 389-393. IEEE, 2014.
Subetha, T., and S. Chitrakala. "A Survey on human activity recognition from videos." In Information Communication and Embedded Systems (ICICES), 2016 International Conference on, pp. 1-7. IEEE, 2016.
Chaquet, Jose M., Enrique J. Carmona, and Antonio Fernández-Caballero. "A survey of video datasets for human action and activity recognition." Computer Vision and Image Understanding, vol. 117, no. 6 pp. 633-659, 2013.
Hsu, Chih-Wei, and Chih-Jen Lin. "A comparison of methods for multiclass support vector machines." IEEE transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
Chang, Chih-Chung. " LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 27, pp. 1-27, 2011. http://www. csie. ntu. edu. tw/~ cjlin/libsvm 2 (2011).
Chen, Chen, Roozbeh Jafari, and Nasser Kehtarnavaz. "Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor." In Image Processing (ICIP), 2015 IEEE International Conference, pp. 168-172. IEEE, 2015, ISBN: 978-1-4799-8339-1/15/$31.00.
Copyright (c) 2019 International Journal of Intelligent Systems and Applications in Engineering
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.