Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

AI for Analysis of Human Behavior

Author : Manas Kumar Hati 1

Date of Publication :20th May 2017

Abstract: Since human and other animals exhibit Natural Intelligence (NI), for programing machine Artificial Intelligence (AI) is used. The field of AI research is described as the study of smart devices such as a robot, computer etc. that take necessary action according to surrounding environment. Moving object is a prominent area of computer-vision research in the field of video surveillance system. This is not effortless work as persistent manipulation of the subject happens through travel. Across spatial and temporal spaces, each object across motion has different accreditations. In temporal space entity the motion rate varies where as in space entity the size differs. Detection and recognition of individuals is the main focus of this research. Community of people existing video sets are known for the purpose of identifying people and tracking people in crowd scene. The method of context subtraction is used to identify humans. To remove information, the descriptor method of the Oriented Gradient Histogram is applied. Support Vector Machine (SVM) classification system is used to classify the conducted human activity.

Reference :

    1. A. Intelligence, “Fundamentals of Neural Networks Artificial Intelligence Fundamentals of Neural Networks Artificial Intelligence,” Fundam. Neural Networks AI Course Lect. 37 – 38, notes, slides, 2010.
    2. J. Li, F. Zhang, L. Wei, T. Yang, and Z. Li, “Cube surface modeling for human detection in crowd,” 2017, doi: 10.1109/ICME.2017.8019311
    3. T. Ji, L. Liu, W. Zhu, J. Wei, and S. Wei, “Fast and efficient integration of human upper-body detection and orientation estimation in RGB-D video,” 2017, doi: 10.1109/ICCSN.2017.8230296.
    4. S. Rohatagi, D. Profit, A. Hatch, C. Zhao, J. P. Docherty, and T. S. Peters-Strickland, “Optimization of a digital medicine system in psychiatry,” J. Clin. Psychiatry, 2016, doi: 10.4088/JCP.16m10693. 
    5. M. Shah and R. Kapdi, “Object detection using deep neural networks,” 2017, doi: 10.1109/ICCONS.2017.8250570
    6. H. Takada, K. Hotta, and P. Janney, “Human tracking in crowded scenes using target information at previous frames,” 2016, doi: 10.1109/ICPR.2016.7899899.
    7. M. Thida, Y. L. Yong, P. Climent-Pérez, H. L. Eng, and P. Remagnino, “A literature review on video analytics of crowded scenes,” in Intelligent Multimedia Surveillance: Current Trends and Research, 2013.
    8. M. A. Torkamani and D. Lowd, “On robustness and regularization of structural support vector machines,” in 31st International Conference on Machine Learning, ICML 2014, 2014, vol. 3, pp. 1989–1999.

Recent Article