Activity detection of untrimmed CCTV ATM footage using 3D convolutional neural network
2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020, Page: 357-362
2020
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures3
- Readers3
Conference Paper Description
This paper presents an approach to temporal human activity detection using the proposal then classification framework, which is one of the frameworks for temporal activity detection. The goal of this research is to detect and recognize certain activities at the ATM. We propose an activity detection method using a 3D convolutional neural network (3D CNN). Our proposed method achieved performance with the accuracy score of 93.94%, a precision of 96.36%, a recall of 93.94%, and an f-score of 93.69%.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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