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Chauhan, N. C.
- A Comprehensive Study of Group Activity Recognition Methods in Video
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Authors
Affiliations
1 Department of Computer Engineering, CHARUSAT University, CHARUSAT Campus, Changa – 388421, Gujarat, IN
2 Department of Information Technology, A. D. Patel Institute of Technology, New V. V. Nagar – 388121, Gujarat, IN
1 Department of Computer Engineering, CHARUSAT University, CHARUSAT Campus, Changa – 388421, Gujarat, IN
2 Department of Information Technology, A. D. Patel Institute of Technology, New V. V. Nagar – 388121, Gujarat, IN
Source
Indian Journal of Science and Technology, Vol 10, No 23 (2017), Pagination:Abstract
Objectives: To provide comprehensive review of different group activity recognition methods, categorize them and provide path to new researcher in this domain. Methods/Statistical Analysis: Different methods of group activity recognition categorized and analyzed according to hand-crafted and learned feature descriptors. Pros and cons of each method are presented. Methods are analyzed in detailed by finding its local level features to global level feature descriptors used along with performance on benchmark dataset. Findings: Different models of group activity recognition are characterized as per the capabilities of the defined model considering individual pose of person, atomic activity of person, person-person interaction, person-group interaction, group-group interaction, uses of temporal information, and recognition of group activity frame wise or video wise. This comprehensive review provides brief information about group activity recognition methods and can be used as brief literature review to the researcher seeking the facts and findings in the field of computer vision in group activity recognition. Applications/Improvements: This reviews help in different applications of human activity analysis, mainly in group activity recognition and the models described here can be used in different applications such running or walking on pathways, waiting at public places, queuing in line in group and many more group activity applications for further enhancement.Keywords
Context Model, Convolution Neural Network, Group Activity Recognition, Group Descriptor, Interaction ModelFull Text
- Predicting Institute Graduation Rate with Genetic Algorithm Assisted Regression for Education Data Mining
Abstract Views :177 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Gujarat Technological University, IN
2 Technology Department, Illinois State University, US
1 Department of Information Technology, Gujarat Technological University, IN
2 Technology Department, Illinois State University, US
Source
ICTACT Journal on Soft Computing, Vol 11, No 2 (2021), Pagination: 2266-2278Abstract
In recent era of Digitization, large amount of computer generated data is accumulated on servers. However, these gathered data is useful only when interesting, novel and applicable knowledge is generated out of it. In the field of education, digitized data is generated by online academic activities. Data mining is discussed with every aspect of society however its use in academia is at infancy. Data mining is used to find novel, interesting and useful knowledge out of data which directs to actionable patterns on which academicians could work to enhance the productivity of academic activities. Education data mining is focused to use educational data for knowledge discovery to attain valuable insights in education domain. In this paper, an important task of prediction of institute graduation rate is addressed. Two novel approaches are proposed in the paper for effective graduation rate prediction. The first approach is genetic algorithm assisted regression model. The second approach investigates and uses various filter methods to further enhance the results in terms of time and number of features. Three regression models – multiple linear regression, decision tree regression and support vector regression are considered for experiments and comparative results are produced. The proposed methods provide better institute graduation rate prediction.Keywords
Genetic Algorithm, Regression, Education Data Mining, Filter Method.
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