Intelligent Human Anomaly Detection using LSTM Autoencoders

Unsupervised deep learning techniques have emerged as powerful tools for detecting anomalies in human behavior patterns, offering a promising solution to the persistent challenge of securing sensitive information. This paper presents a novel approach to human anomaly detection utilizing a Convolutional Long Short-Term Memory (Conv-LSTM) Encoder-Decoder model. The proposed system addresses the critical need for accurate anomaly detection in diverse human activities such as surveillance, healthcare monitoring, and industrial safety. The Conv-LSTM Encoder-Decoder model is adept at learning spatiotemporal patterns in video sequences, enabling the detection of anomalous behaviors effectively. The model’s architecture incorporates convolutional layers to capture spatial information and LSTM layers to model temporal dependencies, facilitating robust anomaly detection. Experimental evaluation of the UCSD Anomaly Detection Dataset demonstrates the system’s capability to accurately identify anomalies while minimizing false positives. The proposed human anomaly detection system autonomously identifies deviations from normal human behavior without manual labeling, contributing to improved security and safety across diverse applications.