Multimedia Datasets for Anomaly Detection: A Review
It addresses the need for a comprehensive analysis of public datasets in anomaly detection for researchers, but it is incremental as it reviews existing datasets without introducing new methods or data.
This paper reviews multimedia datasets for anomaly detection, providing a comprehensive survey of video, audio, and audio-visual datasets to address the lack of comparison and assist researchers in selecting datasets for benchmarking.
Multimedia anomaly datasets play a crucial role in automated surveillance. They have a wide range of applications expanding from outlier objects/ situation detection to the detection of life-threatening events. For more than 1.5 decades, this field has attracted a lot of research attention, and as a result, more and more datasets dedicated to anomalous actions and object detection have been developed. Tapping these public anomaly datasets enable researchers to generate and compare various anomaly detection frameworks with the same input data. This paper presents a comprehensive survey on a variety of video, audio, as well as audio-visual datasets based on the application of anomaly detection. This survey aims to address the lack of a comprehensive comparison and analysis of multimedia public datasets based on anomaly detection. Also, it can assist researchers in selecting the best available dataset for bench-marking frameworks. Additionally, we discuss gaps in the existing dataset and insights for future direction towards developing multimodal anomaly detection datasets.