Abnormal human activity detection by convolutional recurrent neural network using fuzzy logic
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-15904-x.pdf
Reference48 articles.
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2. Biswas S, Gall J (2018) Structural recurrent neural network (SRNN) for group activity analysis. Proc - 2018 IEEE Winter Conf Appl Comput Vision, WACV 2018 2018-January, pp 1625–1632. https://doi.org/10.1109/WACV.2018.00180
3. Chandrakala S, Deepak K, Vignesh LKP (2022) Bag-of-event-models based embeddings for detecting anomalies in surveillance videos. Expert Syst Appl 190:116168. https://doi.org/10.1016/j.eswa.2021.116168
4. Colque RVHM, Caetano C, De Andrade MTL, Schwartz WR (2017) Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos. IEEE Trans Circuits Syst Video Technol 27:673–682. https://doi.org/10.1109/TCSVT.2016.2637778
5. Crispim-Junior CF, Buso V, Avgerinakis K et al (2016) Semantic event fusion of different visual modality concepts for activity recognition. IEEE Trans Pattern Anal Mach Intell 38:1598–1611. https://doi.org/10.1109/TPAMI.2016.2537323
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1. Real-time detection of abnormal human activity using deep learning and temporal attention mechanism in video surveillance;Multimedia Tools and Applications;2023-12-05
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