Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes
This work addresses public safety by improving anomaly detection in crowded video scenes, though it is incremental as it builds on existing functional data analysis and autoencoder approaches.
The study tackled video anomaly detection in crowded scenes by applying the Magnitude-Shape Plot framework to analyze reconstruction errors from autoencoders, achieving better performance than traditional and state-of-the-art methods on benchmark datasets like UCSD Ped2 and CUHK Avenue.
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.