# Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data

arXiv: https://arxiv.org/abs/1809.02728 · HTML: https://mlsanity.com/paper/1809.02728/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.08415/) (2022)
- [CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection](https://mlsanity.com/paper/2412.18820/) (2024)
- [Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility](https://mlsanity.com/paper/2211.14885/) (2022)
- [A principled distributional approach to trajectory similarity measurement](https://mlsanity.com/paper/2301.00393/) (2023)

## Abstract (arXiv)

Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose we introduce an infinite Gaussian mixture model coupled with (bi-directional) generative adversarial networks, IGMM-GAN, that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.