Situational anomaly detection in multimedia data under concept drift

Abstract

Anomaly detection has been a very challenging and active area of research for decades, particularly for video surveillance. However, most of the works detect predefined anomaly classes using static models. These frameworks have limited applicability for real-life surveillance where the data have concept drift. Under concept drift, the distribution of both normal and anomaly classes changes over time. An event may change its class from anomaly to normal or vice-versa. The non-adaptive frameworks do not handle this drift. Additionally, the focus has been on detecting local anomalies, such as a region of an image. In contrast, in CCTV-based monitoring, flagging unseen anomalous situations can be of greater interest. Utilizing multiple sensory information for anomaly detection has also received less attention. This extended abstract discusses these gaps and possible solutions.