Real-Time Anomaly Detection With HMOF Feature
This addresses the problem of computational inefficiency in video surveillance for real-time applications, though it appears incremental as it builds on existing methods like optical flow and auto-encoders.
The paper tackled real-time anomaly detection in video surveillance by proposing a Histogram of Magnitude Optical Flow (HMOF) feature and a framework using auto-encoders and Gaussian Mixture Model classifiers, achieving state-of-the-art performance with reliable real-time detection.
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.