Summary

HOG3D vectors extracted on the bounding boxes of players in videos of tennis and badminton

License

ODbL

Dependencies

Tags

action-recognition computer-vision HOG3D transfer-learning

Attribute Types

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Original Data Format

tgz

Name

Version mldata

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Names

Data (first 10 data points)

(Zipped) TAR archive

ACASVA_actions, ACASVA_actions/BMSB08_300.zip, ACASVA_actions/TWSJ09_960.zip, ACASVA_actions/TMSA03_960.zip, ACASVA_actions/TWSJ09_300.zip, ACASVA_actions/TWDU06_960.tgz, ACASVA_actions/TWDA09_960.zip, ACASVA_actions/BMSB08_960.zip, ACASVA_actions/TWDU06_300.tgz, ACASVA_actions/TWSA03_300.zip, ACASVA_actions/TWDA09_300.zip, ACASVA_actions/ACASVA_actions.html, ACASVA_actions/TMSA03_300.zip, ACASVA_actions/TWSA03_960.zip

Description

Following [deCampos et al, WACV2011], we used HOG3D descriptors extracted on player bounding boxes.

Two different sets of feature extraction parameters were used: the 960D parameters (4x4x3x20) optimised for the KTH dataset and the 300D parameters (2x2x5x5x3) optimised for the Hollywood dataset (see Alexander Klaser's page for details). In our preliminary experiments, we found that the KTH parameters (960D) give better results for the tennis dataset.

• labels.txt: contains action labels; Non-Hit (0), Hit (1) and Serve (2);
• frames.txt: for each sample, it indicates the time stamp of the original video when the features were extracted - note that multiple players are visible in each frame and for this reason consecutive lines have the same frame number;
• teams.txt: represents players as Far player (0) and Near player (1) where far and near players are decided based on the player's feet position in relative to court's mid-line;
• features.txt: contains feature vectors which has either 300 or 960 dimensional vectors, extracted using HOG3D - each line represents a feature vector for a action sample. The first element of each line indicates the dimensionality.

URLs

http://kahlan.eps.surrey.ac.uk/acasva/Downloads.html

Publications

Data Source

HOG3D feature extraction method [Klaser et al, BMVC2008] applied to the space-time bounding box of players in videos of tennis and badminton.

Measurement Details

Performance is evaluated by using data from one of the sports video as training and another for testing, i.e., a whole file is used either for training, validation or testing, we do not encourage to use N-fold cross-validation. We encourage users to report results in terms average accuracy, but it may also be relevant to report True Positive, True Negative and False Positive rates for each of the classes. Area under the ROC curve has also been used.

Usage Scenario

Transductive transfer learning

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by teo on 2012-03-07 12:07

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by teo on 2012-09-24 17:50

revision 18

by teo on 2012-09-24 19:37

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