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

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