# Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks

## TL;DR

This paper introduces a sequence-to-sequence neural network model for real-time anomaly detection in human trajectories, addressing challenges of non-stationarity and high dimensionality in trajectory data.

## Contribution

It proposes a novel deep learning architecture specifically designed for real-time anomaly detection in complex trajectory data, demonstrating effectiveness on synthetic datasets.

## Key findings

- Accurately detects deviations from normal trajectory patterns
- Effective on synthetic datasets with diverse trajectories
- Shows promise for real-world security applications

## Abstract

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.