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SurveillanceNet: Spatio-temporal anomaly identification in surveillance videos using two-stream CNN and LSTM.

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Title: SurveillanceNet: Spatio-temporal anomaly identification in surveillance videos using two-stream CNN and LSTM.
Authors: Ghauri, Muhammad Salman1, ms_ghauri@ymail.com, Bajwa, Usama Ijaz1, usamabajwa@cuilahore.edu.pk, Saleem, Gulshan2, gulshnsaleem26@gmail.com, Raza, Rana Hammad3, hammad@pnec.nust.edu.pk, Anwar, Muhammad Waqas4, waqasanwar@gcu.edu.pk
Source: Multimedia Tools & Applications; Nov2025, Vol. 84 Issue 37, p45871-45895, 25p
Database: Applied Science & Technology Source
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  Data: SurveillanceNet: Spatio-temporal anomaly identification in surveillance videos using two-stream CNN and LSTM.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=189934455
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s11042-025-20961-5
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      – Code: eng
        Text: English
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        StartPage: 45871
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      – TitleFull: SurveillanceNet: Spatio-temporal anomaly identification in surveillance videos using two-stream CNN and LSTM.
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            NameFull: Ghauri, Muhammad Salman
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            NameFull: Bajwa, Usama Ijaz
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            NameFull: Saleem, Gulshan
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            NameFull: Raza, Rana Hammad
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            NameFull: Anwar, Muhammad Waqas
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            – D: 25
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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              Value: 84
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              Value: 37
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