Multimedia Datasets for Anomaly Detection: A Survey

• URL: http://arxiv.org/abs/2112.05410v1
• Date: Fri, 10 Dec 2021 09:32:21 GMT
• Title: Multimedia Datasets for Anomaly Detection: A Survey
• Authors: Pratibha Kumari, Anterpreet Kaur Bedi, Mukesh Saini
• Abstract summary: This paper presents a comprehensive survey on a variety of video, audio, as well as audio-visual datasets based on anomaly detection. It aims to address the lack of a comprehensive comparison and analysis of multimedia public datasets based on anomaly detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimedia anomaly datasets play a crucial role in automated surveillance. They have a wide range of applications expanding from outlier object/ situation detection to the detection of life-threatening events. This field is receiving a huge level of research interest for more than 1.5 decades, and consequently, more and more datasets dedicated to anomalous actions and object detection have been created. 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 future direction insights towards developing multimodal anomaly detection datasets.

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