Attentive Spatio-Temporal Representation Learning for Diving Classification

Gagan Kanojia, Sudhakar Kumawat, Shanmuganathan Raman; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Competitive diving is a well recognized aquatic sport in which a person dives from a platform or a springboard into the water. Based on the acrobatics performed during the dive, diving is classified into a finite set of action classes which are standardized by FINA. In this work, we propose an attention guided LSTM-based neural network architecture for the task of diving classification. The network takes the frames of a diving video as input and determines its class. We evaluate the performance of the proposed model on a recently introduced competitive diving dataset, Diving48. It contains over 18000 video clips which covers 48 classes of diving. The proposed model outperforms the classification accuracy of the state-of-the-art models in both 2D and 3D frameworks by 11.54% and 4.24%, respectively. We show that the network is able to localize the diver in the video frames during the dive without being trained with such a supervision.

Related Material


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[bibtex]
@InProceedings{Kanojia_2019_CVPR_Workshops,
author = {Kanojia, Gagan and Kumawat, Sudhakar and Raman, Shanmuganathan},
title = {Attentive Spatio-Temporal Representation Learning for Diving Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}