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Paper Title : HUMAN ACTIVITY RECOGNITION USING MACHINE LEARNING

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.7263493

Authors: Vybhavi K, Himasree K, Srilekha K

         


Abstract
With the increasing number of anti-social events taking place, there has been a recent focus on security. Many organizations have installed CCTV to constantly monitor people and their interactions. In a developed country with a population of 64 million, each person is caught on camera 30 times a day. A large amount of video data generated and stored over a period of time. A 704 x 576 image recorded at 25 frames per second will generate roughly 20GB per day. Continuous monitoring of data by humans to assess whether events are abnormal is an almost impossible task as it requires manpower and their constant attention. This creates a need to automate the same. It is also necessary to show in which frame and which part of it contains the unusual activity, which helps to quickly assess the unusual activity as abnormal. This is done by converting video to images and analyzing people and activating them from the processed image. Machine learning and deep learning algorithms and techniques support us in broad adoption to make it possible.

Reference
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge L. Reyes Ortiz(2012). Human Activity Recognition on Smartphones Using a Multiclass Hardware Friendly Support Vector Machine. Springer International Workshop on Ambient Assisted Living.Lecture notes in Computer Science. Vol(7657), pp 216- 223. [2] Jun Liu, Amir Shahroudy, Dong Xu, Gang Wang(2016). Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition. European Conference on Computer Vision., pp 816-833. Vol(9907) Oyelade, Oladipupo, Obagbuwa(2010). Application aof k-Means Clustering algorithm for prediction of Students’ [3] Academic Performance. International Journal of Computer Science and Information Security,7(1).292-295 [4] JuhaVesanto(1999). SOM-Based Data Visualization Methods. Intelligent Data Analysis, Laboratory of Computer and Information Science, Helsinki University of Technology, P. O. Box 5400, FIN-02015 HUT, Finlandvol. 3(2), pp. 111-126. [5] Ivan Viola(2010). Information Theory in Computer Graphics and Visualization. Proceeding in SA’11SIGGRAPH Asia 2011 Courses. [6] Jiang, L., Zhang, H., &Cai, Z. (2009). A novel Bayes model: Hidden NaïveBayes.IEEE Transactions on Knowledge and Data Engineering, 21(10),1361–1371.
Keywords
— HUMAN ACTIVITY RECOGNITION USING MACHINE LEARNING