Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets

TL;DR

This paper investigates the impact of concept drift on multimedia anomaly detection, proposing an extended AGMM framework that better adapts to changing data distributions, supported by new facial datasets with long-term age variations.

Contribution

It introduces a modified AGMM that retains past information longer to handle concept drift and provides new facial datasets for long-term anomaly detection research.

Findings

Extended AGMM outperforms baseline in handling concept drift

New facial datasets with long-term age variation created for research

Proposed framework improves anomaly detection accuracy over time

Abstract

Anomaly detection in multimedia datasets is a widely studied area. Yet, the concept drift challenge in data has been ignored or poorly handled by the majority of the anomaly detection frameworks. The state-of-the-art approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can drift from one class to another in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we systematically investigate the effect of concept drift on various detection models and propose a modified Adaptive Gaussian Mixture Model (AGMM) based framework for anomaly detection in multimedia data. In contrast to the baseline AGMM, the proposed extension of AGMM remembers the past for a longer period in order to handle the drift better. Extensive experimental analysis shows that the proposed model better handles the drift in data as compared with the baseline AGMM. Further, to facilitate research and comparison with the proposed framework, we contribute three multimedia datasets constituting faces as samples. The face samples of individuals correspond to the age difference of more than ten years to incorporate a longer temporal context.