# Unsupervised Anomaly Detection from Time-of-Flight Depth Images

arXiv: https://arxiv.org/abs/2203.01052 · HTML: https://mlsanity.com/paper/2203.01052/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Anomaly Detection in Video via Self-Supervised and Multi-Task Learning](https://mlsanity.com/paper/2011.07491/) (2020)
- [Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images](https://mlsanity.com/paper/1804.04488/) (2018)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)
- [Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos](https://mlsanity.com/paper/1903.03295/) (2019)
- [SiT: Self-supervised vIsion Transformer](https://mlsanity.com/paper/2104.03602/) (2021)
- [An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos](https://mlsanity.com/paper/1801.03149/) (2018)
- [Driver Anomaly Detection: A Dataset and Contrastive Learning Approach](https://mlsanity.com/paper/2009.14660/) (2020)
- [Any-Shot Sequential Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2004.02072/) (2020)
- [Background Subtraction in Real Applications: Challenges, Current Models and Future Directions](https://mlsanity.com/paper/1901.03577/) (2019)
- [TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera](https://mlsanity.com/paper/2108.12196/) (2021)
- [Using Visual Anomaly Detection for Task Execution Monitoring](https://mlsanity.com/paper/2107.14206/) (2021)
- [Benchmark data and method for real-time people counting in cluttered scenes using depth sensors](https://mlsanity.com/paper/1804.04339/) (2018)

## Built on by (most cited first, 6 of 6)

- [Exploring Diffusion Models for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2304.05841/) (2023)
- [Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2301.00114/) (2022)
- [Privacy-Preserving Video Anomaly Detection: A Survey](https://mlsanity.com/paper/2411.14565/) (2024)
- [Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder](https://mlsanity.com/paper/2306.04466/) (2023)
- [HyPCV-Former: Hyperbolic Spatio-Temporal Transformer for 3D Point Cloud Video Anomaly Detection](https://mlsanity.com/paper/2508.00473/) (2025)
- [Achieving RGB-D level Segmentation Performance from a Single ToF Camera](https://mlsanity.com/paper/2306.17636/) (2023)

## Abstract (arXiv)

Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth data in this context instead is still hardly explored in spite of depth images being a popular choice in many other computer vision research areas and the increasing availability of inexpensive depth camera hardware. We evaluate the application of existing autoencoder-based methods on depth video and propose how the advantages of using depth data can be leveraged by integration into the loss function. Training is done unsupervised using normal sequences without need for any additional annotations. We show that depth allows easy extraction of auxiliary information for scene analysis in the form of a foreground mask and demonstrate its beneficial effect on the anomaly detection performance through evaluation on a large public dataset, for which we are also the first ones to present results on.