Discriminative context learning with gated recurrent unit for group activity recognition

In this study, we address the problem of similar local motions that create confusion within different group activities. To reduce the influences of motions, we propose a discriminative group context feature (DGCF) that considers prominent sub-events. Moreover, we adopt a gated recurrent unit (GRU) model that can learn temporal changes in a sequence. In real-world scenarios, people perform activities with different temporal lengths. The GRU model handles an arbitrary length of data for training with nonlinear hidden units in the network. However, when we use a deep neural network model, data scarcity causes overfitting problems. Data augmentation methods for images are ineffective for trajectory data augmentation. Thus, we also propose a method for trajectory augmentation. We evaluate the effectiveness of the proposed method on three datasets. In our experiments on each dataset, we show that the proposed method outperforms the competing state-of-the-art methods for group activity recognition.