首页|Studies from National Institute of Technology Rourkela Have Provided New Information about Artificial Intelligence (A Comprehensive Review of Datasets for Detection and Localization of Video Anomalies: a Step Towards Data-centric Artificial ...)
Studies from National Institute of Technology Rourkela Have Provided New Information about Artificial Intelligence (A Comprehensive Review of Datasets for Detection and Localization of Video Anomalies: a Step Towards Data-centric Artificial ...)
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NETL
NSTL
Springer Nature
Investigators publish new report on Artificial Intelligence. According to news reporting originating from Odisha, India, by NewsRx correspondents, research stated, “Video anomaly detection and localization is one of the key components of the intelligent video surveillance system. Video anomaly detection refers to the process of spatiotemporal localization of the abnormal or anomalous pattern present in the video.” Funders for this research include IMPACTING RESEARCH INNOVATION AND TECHNOLOGY (IMPRINT) INDIA, Ministry of Human Resource Development (MHRD), Government of India, Ministry of Housing and Urban Affairs, Government of India. Our news editors obtained a quote from the research from the National Institute of Technology Rourkela, “The performance of the deep learning-based video anomaly detector depends on the quality and quantity of the video anomaly datasets used for training. However, there is a scarcity of effective video anomaly datasets due to inherent natures such as rareness, context-dependency, and equivocal nature. Further, state-of-theart lacks a review that presents a comprehensive study of video anomaly datasets, including issues associated with the existing datasets, comparative analysis of the available datasets, potential solutions using both model-centric and data-centric approaches. Hence, a comprehensive review of the publicly available video anomaly datasets for video anomaly detection and localization is presented in this article. Further, a comparative study of the existing video anomaly datasets at qualitative and quantitative levels is presented to decide the right strategies for the desired application. Subsequently, model-centric and data-centric approaches required to solve various problems associated with the video anomaly datasets are presented.”
OdishaIndiaAsiaArtificial IntelligenceEmerging TechnologiesMachine LearningNational Institute of Technology Rourkela
NETL
NSTL
Springer Nature
