End-to-End Key-Player-Based Group Activity Recognition Network Applied to Basketball Offensive Tactic Identification in Limited Data Scenarios
Journal
IEEE Access
Journal Volume
9
Pages
104395-104404
Date Issued
2021
Author(s)
Abstract
In this paper, we propose an end-to-end key-player-based group activity recognition network specially applied to the identification of basketball offensive tactics in limited data scenarios. Our previous studies show that basketball tactics can be better recognized via key player detection with multiple instance learning (MIL) using the support vector machine (SVM). However, the SVM in that work is required to extract features depending on basketball- and tactic-specific knowledge for good performance. Thus, in this study, we develop an end-to-end trainable neural network without prior knowledge and integrate MIL into it. As long as a tactic label is given, MIL can train the network to identify tactic's key players. For testing, our network can recognize the key players in a video clip and provide a tag of the tactic related to them. Like other neural network models, our network requires a large annotated dataset. At the same time, we could collect only a few labeled data, which is common in dealing with group activity recognition. To overcome such a limitation, we propose a novel data augmentation framework, the tactical-based conditional generative adversarial network (GAN), for generating new labeled trajectories. The experimental results show that our method significantly improves 9.13 % in tactic recognition and 4.965 % in key player detection. © 2013 IEEE.
Subjects
Data augmentation; end-to-end deep neural networks; generative adversarial networks; group activity recognition; key player detection; multiple instance learning; sports video analysis
Other Subjects
Basketball; Large dataset; Learning systems; Pattern recognition; Support vector machines; Adversarial networks; Data augmentation; Group activity recognition; Multiple-instance learning; Neural network model; Player detections; Prior knowledge; Specific knowledge; Neural networks
Type
journal article