1 research outputs found
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks
Detection of anomalous trajectories is an important problem with potential
applications to various domains, such as video surveillance, risk assessment,
vessel monitoring and high-energy physics. Modeling the distribution of
trajectories with statistical approaches has been a challenging task due to the
fact that such time series are usually non stationary and highly dimensional.
However, modern machine learning techniques provide robust approaches for
data-driven modeling and critical information extraction. In this paper, we
propose a Sequence to Sequence architecture for real-time detection of
anomalies in human trajectories, in the context of risk-based security. Our
detection scheme is tested on a synthetic dataset of diverse and realistic
trajectories generated by the ISL iCrowd simulator. The experimental results
indicate that our scheme accurately detects motion patterns that deviate from
normal behaviors and is promising for future real-world applications.Comment: AVSS 201