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TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval

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arxiv 2009.09984 v1 pith:6YSSTTO6 submitted 2020-09-21 cs.CV cs.AI

George Awad , Asad A. Butt , Keith Curtis , Yooyoung Lee , Jonathan Fiscus , Afzal Godil , Andrew Delgado , Jesse Zhang
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Eliot Godard Lukas Diduch Alan F. Smeaton Yvette Graham Wessel Kraaij Georges Quenot
This is my paper · ORCID
classification cs.CVcs.AI
keywords videoevaluationtrecvidretrievalsearchtasksbenchmarkeffort
verification ladder T0 review T1 audit T2 compute T3 formal
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The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last nineteen years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2019 represented a continuation of four tasks from TRECVID 2018. In total, 27 teams from various research organizations worldwide completed one or more of the following four tasks: 1. Ad-hoc Video Search (AVS) 2. Instance Search (INS) 3. Activities in Extended Video (ActEV) 4. Video to Text Description (VTT) This paper is an introduction to the evaluation framework, tasks, data, and measures used in the workshop.

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