# Anomaly detection in non-stationary videos using time-recursive differencing network based prediction

arXiv: https://arxiv.org/abs/2503.02234 · HTML: https://mlsanity.com/paper/2503.02234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Latent Space Autoregression for Novelty Detection](https://mlsanity.com/paper/1807.01653/) (2018)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)
- [Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection](https://mlsanity.com/paper/2003.06780/) (2020)
- [Abnormal Event Detection in Videos using Generative Adversarial Nets](https://mlsanity.com/paper/1708.09644/) (2017)
- [Learning Deep Representations of Appearance and Motion for Anomalous Event Detection](https://mlsanity.com/paper/1510.01553/) (2015)
- [A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction](https://mlsanity.com/paper/1512.06293/) (2015)
- [Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection](https://mlsanity.com/paper/1706.03038/) (2017)
- [An Introductory Study on Time Series Modeling and Forecasting](https://mlsanity.com/paper/1302.6613/) (2013)
- [Real-Time Anomaly Detection and Localization in Crowded Scenes](https://mlsanity.com/paper/1511.06936/) (2015)
- [Real-Time Anomaly Detection With HMOF Feature](https://mlsanity.com/paper/1812.04980/) (2018)
- [Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?](https://mlsanity.com/paper/1711.08231/) (2017)

## Abstract (arXiv)

Most videos, including those captured through aerial remote sensing, are usually non-stationary in nature having time-varying feature statistics. Although, sophisticated reconstruction and prediction models exist for video anomaly detection, effective handling of non-stationarity has seldom been considered explicitly. In this paper, we propose to perform prediction using a time-recursive differencing network followed by autoregressive moving average estimation for video anomaly detection. The differencing network is employed to effectively handle non-stationarity in video data during the anomaly detection. Focusing on the prediction process, the effectiveness of the proposed approach is demonstrated considering a simple optical flow based video feature, and by generating qualitative and quantitative results on three aerial video datasets and two standard anomaly detection video datasets. EER, AUC and ROC curve based comparison with several existing methods including the state-of-the-art reveal the superiority of the proposed approach.