Real-Time Anomaly Detection With HMOF Feature

TL;DR

This paper introduces a real-time anomaly detection framework for video surveillance that uses a novel motion-sensitive feature called HMOF, combined with auto-encoders and GMM classifiers, achieving high efficiency and accuracy.

Contribution

The paper proposes the HMOF feature for motion analysis, enabling real-time anomaly detection with low computational cost and improved detection performance.

Findings

HMOF outperforms existing features in anomaly detection tasks.

The framework achieves real-time detection with high accuracy.

Experimental results surpass state-of-the-art methods.

Abstract

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.