Semi-automatic ground truth annotation in videos

Schöning, Julius; Faion, Patrick; Heidemann, Gunther

doi:10.1145/2815833.2816947

Cited by 11 publications

(1 citation statement)

References 9 publications

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“…As the research in autonomous driving accelerates, there will be a need for efficient motion labelling. Schöning et al (2015) provide an interactive solution for the interpolation of polygons over a video sequence. The user sets a seed annotation and is then being asked to correct if the semiautomatic annotations appear to be incorrect.…”

Section: Autonomous Drivingmentioning

confidence: 99%

A survey of image labelling for computer vision applications

Sager

Janiesch

Zschech

2021

Journal of Business Analytics

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Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.

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Section: Autonomous Drivingmentioning

confidence: 99%