# Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction

arXiv: https://arxiv.org/abs/2011.02763 · HTML: https://mlsanity.com/paper/2011.02763/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 30 of 42)

- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles](https://mlsanity.com/paper/2207.10172/) (2022)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Video Event Restoration Based on Keyframes for Video Anomaly Detection](https://mlsanity.com/paper/2304.05112/) (2023)
- [Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2404.08531/) (2024)
- [A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation](https://mlsanity.com/paper/2305.13611/) (2023)
- [Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection](https://mlsanity.com/paper/2303.13051/) (2023)
- [Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection](https://mlsanity.com/paper/2207.10948/) (2022)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement](https://mlsanity.com/paper/2108.01975/) (2021)
- [Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep Skeleton Features](https://mlsanity.com/paper/2303.15167/) (2023)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection](https://mlsanity.com/paper/2209.02899/) (2022)
- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data](https://mlsanity.com/paper/2112.12833/) (2021)
- [Consistency-based Self-supervised Learning for Temporal Anomaly Localization](https://mlsanity.com/paper/2208.05251/) (2022)
- [Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector](https://mlsanity.com/paper/2211.00349/) (2022)
- [VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection](https://mlsanity.com/paper/2409.16225/) (2024)
- [Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder](https://mlsanity.com/paper/2104.11008/) (2021)
- [Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection](https://mlsanity.com/paper/2404.04986/) (2024)
- [Few-Shot Anomaly Detection via Category-Agnostic Registration Learning](https://mlsanity.com/paper/2406.08810/) (2024)
- [Vision-Language Models Assisted Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2409.14109/) (2024)
- [Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks](https://mlsanity.com/paper/2511.19474/) (2025)
- [Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets](https://mlsanity.com/paper/2207.13430/) (2022)
- [An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination](https://mlsanity.com/paper/2309.09436/) (2023)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM](https://mlsanity.com/paper/2507.21649/) (2025)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models](https://mlsanity.com/paper/2511.07299/) (2025)
- [Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions](https://mlsanity.com/paper/2210.13927/) (2022)

## Abstract (arXiv)

Video anomaly detection is commonly used in many applications such as security surveillance and is very challenging.A majority of recent video anomaly detection approaches utilize deep reconstruction models, but their performance is often suboptimal because of insufficient reconstruction error differences between normal and abnormal video frames in practice. Meanwhile, frame prediction-based anomaly detection methods have shown promising performance. In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos. The proposed method is equipped with a multi-path ConvGRU-based frame prediction network that can better handle semantically informative objects and areas of different scales and capture spatial-temporal dependencies in normal videos. A noise tolerance loss is introduced during training to mitigate the interference caused by background noise. Extensive experiments have been conducted on the CUHK Avenue, ShanghaiTech Campus, and UCSD Pedestrian datasets, and the results show that our proposed method outperforms existing state-of-the-art approaches. Remarkably, our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.