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An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Research article (Sensors, 2021) · cited 183× · AI/ML
academia openalex:W3153454949
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An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

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An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos is a scholarly article[1].

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos. Retrieved May 24, 2026, from https://4ort.xyz/entity/an-efficient-anomaly-recognition-framework-using-an-attention-residual-lstm-in-surveillance-videos
MLA “An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/an-efficient-anomaly-recognition-framework-using-an-attention-residual-lstm-in-surveillance-videos.
BibTeX @misc{4ortxyz_an-efficient-anomaly-recognition-framework-using-an-attention-residual-lstm-in-surveillance-videos_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos}}, year = {2026}, url = {https://4ort.xyz/entity/an-efficient-anomaly-recognition-framework-using-an-attention-residual-lstm-in-surveillance-videos}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos — https://4ort.xyz/entity/an-efficient-anomaly-recognition-framework-using-an-attention-residual-lstm-in-surveillance-videos (retrieved 2026-05-24)

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