Pubblicazione scientifica

Attentive Spatio-Temporal Representation Learning for Diving Classification

FILONE TECNOLOGICO	TEMA
sicurezza e qualità sui luoghi di lavoro

RIASSUNTO

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.

DATA

Data di pubblicazione: 01/06/2019

AUTORI

GAGAN KANOJIA
SUDHAKAR KUMAWAT
SHANMUGANATHAN RAMAN

ENTE DI AFFERENZA

INDIAN INST TECH GANDHINAGAR INDIA

RIVISTA

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (Page(s): 2467-2476)

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