Attention-based bidirectional-long short-term memory for abnormal human activity detection

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.

A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

“…A significant number of research works and studies have been conducted to identify and classify human activities by analyzing the motion from video captured through closed-circuit television (CCTV) or other types of camera systems. Machine learning and deep learning models were widely used in many works to identify anomalies in activities [31,32] along with classification [33][34][35]. Since placing surveillance cameras to observe the residents presents data privacy issues/concerns, sensor-based observation has become popular.…”

Efficient pneumonia detection using Vision Transformers on chest X-rays

“…Multiple studies 11 – 14 have demonstrated the efficacy of this method in detecting pneumonia with a high degree of accuracy. Attention mechanism isn DL refers 15 – 21 to a technique used in neural networks to selectively focus on certain portions of an input as opposed to processing the entire input equally. In image detection and classification, attention mechanisms can be utilized to concentrate the network's attention on specific regions of an image that are most important for making a classification decision.…”

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

“…4. Attentive recurrent neural networks Kumar et al (2023): In this technique, a recurrent neural network is used to detect the temporal dynamics of suspicious activities. The network has been shown to be capable of detecting suspicious activity in records that have long stretches of inconspicuous activity.…”