ArticleScientific reports2023
Attention-based bidirectional-long short-term memory for abnormal human activity detection.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 35 citations in OpenAlex.
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- A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition.Scientific reports · 2025Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abnormal human behavior must be monitored and controlled in today's technology-driven era, since it may cause damage to society in the form of assault or web-based violence, such as direct harm to a person or the propagation of hate crimes through the internet. Several authors have attempted to address this issue, but no one has yet come up with a solution that is both practical and workable. Recently, deep learning models have become popular as a means of handling massive amounts of data but their potential to categorize the aberrant human activity remains unexplored. Using a convolutional neural network (CNN), a bidirectional long short-term memory (Bi-LSTM), and an attention mechanism to pay attention to the unique spatiotemporal characteristics of raw video streams, a deep-learning approach has been implemented in the proposed framework to detect anomalous human activity. After analyzing the video, our suggested architecture can reliably assign an abnormal human behavior to its designated category. Analytic findings comparing the suggested architecture to state-of-the-art algorithms reveal an accuracy of 98.9%, 96.04%, and 61.04% using the UCF11, UCF50, and subUCF crime datasets, respectively.
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