# Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks

arXiv: https://arxiv.org/abs/1612.00390 · HTML: https://mlsanity.com/paper/1612.00390/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

## Built on by (most cited first, 24 of 24)

- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Advancing Video Anomaly Detection: A Concise Review and a New Dataset](https://mlsanity.com/paper/2402.04857/) (2024)
- [Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2306.14451/) (2023)
- [Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals](https://mlsanity.com/paper/2107.12626/) (2021)
- [Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead](https://mlsanity.com/paper/2311.02782/) (2023)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2201.00464/) (2022)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Anomaly Detection in Aerial Videos with Transformers](https://mlsanity.com/paper/2209.13363/) (2022)
- [Privacy-Preserving Video Anomaly Detection: A Survey](https://mlsanity.com/paper/2411.14565/) (2024)
- [Consistency-based Self-supervised Learning for Temporal Anomaly Localization](https://mlsanity.com/paper/2208.05251/) (2022)
- [Foundation Models and Transformers for Anomaly Detection: A Survey](https://mlsanity.com/paper/2507.15905/) (2025)
- [A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods](https://mlsanity.com/paper/2111.01604/) (2021)
- [ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos](https://mlsanity.com/paper/2604.09819/) (2026)
- [Multi-Task Learning based Video Anomaly Detection with Attention](https://mlsanity.com/paper/2210.07697/) (2022)
- [The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM](https://mlsanity.com/paper/2507.21649/) (2025)
- [Good Practices and A Strong Baseline for Traffic Anomaly Detection](https://mlsanity.com/paper/2105.03827/) (2021)
- [Anomaly Detection in Satellite Videos using Diffusion Models](https://mlsanity.com/paper/2306.05376/) (2023)
- [Prior Based Online Lane Graph Extraction from Single Onboard Camera Image](https://mlsanity.com/paper/2307.13344/) (2023)
- [Future Video Prediction from a Single Frame for Video Anomaly Detection](https://mlsanity.com/paper/2308.07783/) (2023)
- [LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding](https://mlsanity.com/paper/2205.08362/) (2022)
- [Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset](https://mlsanity.com/paper/2210.07400/) (2022)
- [Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network](https://mlsanity.com/paper/2411.08755/) (2024)

## Abstract (arXiv)

Automating the detection of anomalous events within long video sequences is challenging due to the ambiguity of how such events are defined. We approach the problem by learning generative models that can identify anomalies in videos using limited supervision. We propose end-to-end trainable composite Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to predict the evolution of a video sequence from a small number of input frames. Regularity scores are derived from the reconstruction errors of a set of predictions with abnormal video sequences yielding lower regularity scores as they diverge further from the actual sequence over time. The models utilize a composite structure and examine the effects of conditioning in learning more meaningful representations. The best model is chosen based on the reconstruction and prediction accuracy. The Conv-LSTM models are evaluated both qualitatively and quantitatively, demonstrating competitive results on anomaly detection datasets. Conv-LSTM units are shown to be an effective tool for modeling and predicting video sequences.