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The increasing popularity of compact and inexpensive cameras, e.g.~dashcameras, body cameras, and cameras equipped on robots, has sparked a growinginterest in detecting anomalies within dynamic scenes recorded by movingcameras. However, existing reviews primarily concentrate on Video AnomalyDetection (VAD) methods assuming static cameras. The VAD literature with movingcameras remains fragmented, lacking comprehensive reviews to date. To addressthis gap, we endeavor to present the first comprehensive survey on MovingCamera Video Anomaly Detection (MC-VAD). We delve into the research papersrelated to MC-VAD, critically assessing their limitations and highlightingassociated challenges. Our exploration encompasses three application domains:security, urban transportation, and marine environments, which in turn coversix specific tasks. We compile an extensive list of 25 publicly-availabledatasets spanning four distinct environments: underwater, water surface,ground, and aerial. We summarize the types of anomalies these datasetscorrespond to or contain, and present five main categories of approaches fordetecting such anomalies. Lastly, we identify future research directions anddiscuss novel contributions that could advance the field of MC-VAD. With thissurvey, we aim to offer a valuable reference for researchers and practitionersstriving to develop and advance state-of-the-art MC-VAD methods.

Survey on video anomaly detection in dynamic scenes with moving cameras