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The redundancy of the information in a video contents can be reduced using the process of video event detection. Here the aim is to construct a reliable and scalable solutions for the large scale video event detection systems. With the analytical and statistical learning models used for the existing event detection procedures, its focusing on the low level features of video content. In such methods, the capability of analyzing and interpreting the content of complex video events lacks accuracy. So, the intermediary-level representation of semantic concept of real time events has been introduced as a method for perceiving video events. High accuracy can only be achieved when various features including the high level and low level features of different modalities is used. Event identification can be used effectively in real time video processing technologies. Hence, accuracy rate has to be improved using the newly introduced methodology.
Keywords:
CNN, Tensorflow, SVM
Cite Article:
"Semantic Event Detection In Videos ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.4, Issue 3, page no.40 - 43, March-2019, Available :http://www.ijrti.org/papers/IJRTI1903009.pdf
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000204769
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator