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A large-Scale TV Dataset for partial video copy detection

A large-Scale TV Dataset for partial video copy detection

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Abstract

This paper is interested with the performance evaluation of the partial video copy detection. Several public datasets exist designed from web videos. The detection problem is inherent to the continuous video broadcasting. The alternative is then to process with TV datasets offering a deeper scalability and a control of degradations for a fine performance evaluation. We propose in this paper a TV dataset called STVD. It is designed with a protocol ensuring a scalable capture and robust groundtruthing. STVD is the largest public dataset on the task with a near 83k videos having a total duration of 10,660 hours. Performance evaluation results of representative methods on the dataset are reported in the paper for a baseline comparison.
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Dates and versions

hal-03638514 , version 1 (12-04-2022)

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  • HAL Id : hal-03638514 , version 1

Cite

Van-Hao Le, Mathieu Delalandre, Donatello Conte. A large-Scale TV Dataset for partial video copy detection. International Conference on Image Analysis and Processing (ICIAP), May 2022, Lecce, Italy. ⟨hal-03638514⟩
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