Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes

TL;DR

This paper introduces a novel anomaly detection framework for crowded videos using the Magnitude-Shape Plot within a Functional Data Analysis approach, leveraging autoencoders and statistical analysis to improve detection accuracy.

Contribution

It presents a new MS-Plot-based methodology for analyzing reconstruction errors, offering a statistically principled and interpretable approach for video anomaly detection.

Findings

Outperforms traditional univariate functional detectors.

Achieves better accuracy than several state-of-the-art methods.

Demonstrates promising results on benchmark datasets.

Abstract

Detecting anomalies in crowded video scenes is critical for public safety, enabling timely identification of potential threats. This study explores video anomaly detection within a Functional Data Analysis framework, focusing on the application of the Magnitude-Shape (MS) Plot. Autoencoders are used to learn and reconstruct normal behavioral patterns from anomaly-free training data, resulting in low reconstruction errors for normal frames and higher errors for frames with potential anomalies. The reconstruction error matrix for each frame is treated as multivariate functional data, with the MS-Plot applied to analyze both magnitude and shape deviations, enhancing the accuracy of anomaly detection. Using its capacity to evaluate the magnitude and shape of deviations, the MS-Plot offers a statistically principled and interpretable framework for anomaly detection. The proposed methodology is evaluated on two widely used benchmark datasets, UCSD Ped2 and CUHK Avenue, demonstrating promising performance. It performs better than traditional univariate functional detectors (e.g., FBPlot, TVDMSS, Extremal Depth, and Outliergram) and several state-of-the-art methods. These results highlight the potential of the MS-Plot-based framework for effective anomaly detection in crowded video scenes.