# Real-Time Anomaly Detection With HMOF Feature

arXiv: https://arxiv.org/abs/1812.04980 · HTML: https://mlsanity.com/paper/1812.04980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos](https://mlsanity.com/paper/2503.00670/) (2025)
- [Anomaly detection in non-stationary videos using time-recursive differencing network based prediction](https://mlsanity.com/paper/2503.02234/) (2025)

## Abstract (arXiv)

Anomaly detection is a challenging problem in intelligent video surveillance. Most existing methods are computation consuming, which cannot satisfy the real-time requirement. In this paper, we propose a real-time anomaly detection framework with low computational complexity and high efficiency. A new feature, named Histogram of Magnitude Optical Flow (HMOF), is proposed to capture the motion of video patches. Compared with existing feature descriptors, HMOF is more sensitive to motion magnitude and more efficient to distinguish anomaly information. The HMOF features are computed for foreground patches, and are reconstructed by the auto-encoder for better clustering. Then, we use Gaussian Mixture Model (GMM) Classifiers to distinguish anomalies from normal activities in videos. Experimental results show that our framework outperforms state-of-the-art methods, and can reliably detect anomalies in real-time.