# Real-Time Anomaly Detection and Localization in Crowded Scenes

arXiv: https://arxiv.org/abs/1511.06936 · HTML: https://mlsanity.com/paper/1511.06936/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2015.

## Built on by (most cited first, 20 of 20)

- [Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2303.12369/) (2023)
- [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)
- [Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection](https://mlsanity.com/paper/2207.10948/) (2022)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Discrete neural representations for explainable anomaly detection](https://mlsanity.com/paper/2112.05585/) (2021)
- [Image/Video Deep Anomaly Detection: A Survey](https://mlsanity.com/paper/2103.01739/) (2021)
- [X-MAN: Explaining multiple sources of anomalies in video](https://mlsanity.com/paper/2106.08856/) (2021)
- [VADMamba: Exploring State Space Models for Fast Video Anomaly Detection](https://mlsanity.com/paper/2503.21169/) (2025)
- [ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables](https://mlsanity.com/paper/2202.10281/) (2022)
- [Instance-Aligned Captions for Explainable Video Anomaly Detection](https://mlsanity.com/paper/2601.08155/) (2026)
- [Human-centric Behavior Description in Videos: New Benchmark and Model](https://mlsanity.com/paper/2310.02894/) (2023)
- [Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection](https://mlsanity.com/paper/2409.17608/) (2024)
- [Multiple Instance Learning for Cheating Detection and Localization in Online Examinations](https://mlsanity.com/paper/2402.06107/) (2024)
- [VADMamba++: Efficient Video Anomaly Detection via Hybrid Modeling in Grayscale Space](https://mlsanity.com/paper/2604.00360/) (2026)
- [Abnormal Behavior Detection Based on Target Analysis](https://mlsanity.com/paper/2107.13706/) (2021)
- [Enhance Multi-Scale Spatial-Temporal Coherence for Configurable Video Anomaly Detection](https://mlsanity.com/paper/2305.07328/) (2023)
- [Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services](https://mlsanity.com/paper/2307.10653/) (2023)
- [Anomaly detection in non-stationary videos using time-recursive differencing network based prediction](https://mlsanity.com/paper/2503.02234/) (2025)

## Abstract (arXiv)

In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is described using two local and global descriptors. These descriptors capture the video properties from different aspects. By incorporating simple and cost-effective Gaussian classifiers, we can distinguish normal activities and anomalies in videos. The local and global features are based on structure similarity between adjacent patches and the features learned in an unsupervised way, using a sparse auto- encoder. Experimental results show that our algorithm is comparable to a state-of-the-art procedure on UCSD ped2 and UMN benchmarks, but even more time-efficient. The experiments confirm that our system can reliably detect and localize anomalies as soon as they happen in a video.