RAVEL: an annotated corpus for training robots with audiovisual abilities

Alameda-Pineda, Xavier; Sanchez-Riera, Jordi; Wienke, Johannes; Čech, Jan; Kulkarni, Kaustubh; Deleforge, Antoine; Horaud, Radu

doi:10.1007/s12193-012-0111-y

Cited by 33 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the performance of the proposed binocular method and compare it with a state-of-the-art monocular method [3], we use the Ravel 2 dataset [24]. The Ravel dataset consists of 7 actions (talk phone, drink, scratch head, turn around, check watch, clap, cross arms) performed by 12 actors in 6 trials each.…”

Section: Methodsmentioning

confidence: 99%

Action Recognition Robust to Background Clutter by Using Stereo Vision

Sanchez-Riera¹,

Čech²,

Horaud³

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

Self Cite

View full text Add to dashboard Cite

Abstract. An action recognition algorithm which works with binocular videos is presented. The proposed method uses standard bag-of-words approach, where each action clip is represented as a histogram of visual words. However, instead of using classical monocular HoG/HoF features, we construct features from the scene-flow computed by a matching algorithm on the sequence of stereo images. The resulting algorithm has a comparable or slightly better recognition accuracy than standard monocular solution in controlled setup with a single actor present in the scene. However, we show its significantly improved performance in case of strong background clutter due to other people freely moving behind the actor.

show abstract

Section: Methodsmentioning

confidence: 99%

Action Recognition Robust to Background Clutter by Using Stereo Vision

Sanchez-Riera¹,

Čech²,

Horaud³

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this, we selected the "Robot Gesture" scenario of the RAVEL dataset [10]. We evaluated the methods splitting actor-wise the dataset into a training subset and a testing subset several times, following a standard cross-validation strategy.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking methods for audio-visual recognition using tiny training sets

Alameda-Pineda

Sanchez-Riera

Horaud

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Self Cite

View full text Add to dashboard Cite

The problem of choosing a classifier for audio-visual command recognition is addressed. Because such commands are culture-and user-dependant, methods need to learn new commands from a few examples. We benchmark three state-ofthe-art discriminative classifiers based on bag of words and SVM. The comparison is made on monocular and monaural recordings of a publicly available dataset. We seek for the best trade off between speed, robustness and size of the training set. In the light of over 150,000 experiments, we conclude that this is a promising direction of work towards a flexible methodology that must be easily adaptable to a large variety of users.

show abstract

“…For dynamic scenarios, the AV16.3 dataset [29] and [30] involve multiple moving human talkers. The RAVEL and CAMIL datasets [31], [32] provide camera and microphone recordings from a rotating robot head. However, annotation of the ground-truth source positions is typically performed in a semi-automatic manner, where humans label bounding boxes on small video segments.…”

Section: Locata Challenge Tasksmentioning

confidence: 99%

The LOCATA Challenge: Acoustic Source Localization and Tracking

Evers,

Loellmann,

Mellmann

et al. 2019

Preprint

View full text Add to dashboard Cite

The ability to localize and track acoustic events is a fundamental prerequisite for equipping machines with the ability to be aware of and engage with humans in their surrounding environment. However, in realistic scenarios, audio signals are adversely affected by reverberation, noise, interference, and periods of speech inactivity. In dynamic scenarios, where the sources and microphone platforms may be moving, the signals are additionally affected by variations in the source-sensor geometries. In practice, approaches to sound source localization and tracking are often impeded by missing estimates of active sources, estimation errors, as well as false estimates, diverting from the true source positions. The LOCAlization and TrAcking (LOCATA) Challenge is aiming at an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking. This paper provides a review of relevant localization and tracking algorithms, and, within the context of the existing literature, a detailed evaluation and dissemination of the LOCATA submissions. The evaluation highlights achievements in the field, open challenges, and identifies potential future directions.

show abstract

RAVEL: an annotated corpus for training robots with audiovisual abilities

Cited by 33 publications

References 36 publications

Action Recognition Robust to Background Clutter by Using Stereo Vision

Action Recognition Robust to Background Clutter by Using Stereo Vision

Benchmarking methods for audio-visual recognition using tiny training sets

The LOCATA Challenge: Acoustic Source Localization and Tracking

Contact Info

Product

Resources

About