Spatiotemporal Multimodal Learning With 3D CNNs for Video Action Recognition

Extracting effective spatial-temporal information is significantly important for video-based action recognition. Recently 3D convolutional neural networks (3D CNNs) that could simultaneously encode spatial and temporal dynamics in videos have made considerable progress in action recognition. However, almost all existing 3D CNN-based methods recognize human actions only using RGB videos. The single modality may limit the performance capacity of 3D networks. In this paper, we extend 3D CNN to depth and pose data besides RGB data to evaluate its capacity for spatiotemporal multimodal learning for video action recognition. We propose a novel multimodal two-stream 3D network framework, which can exploit complementary multimodal information to improve the recognition performance. Specifically, we first construct two discriminative video representations under depth and pose data modalities respectively, referred as depth residual dynamic image sequence (DRDIS) and pose estimation map sequence (PEMS). DRDIS captures spatial-temporal evolution of actions in depth videos by progressively aggregating the local motion information. PEMS eliminates the interference of cluttered backgrounds and describes the spatial configuration of body parts intuitively. The multimodal two-stream 3D CNN deals with two separate data streams to learn spatiotemporal features from DRDIS and PEMS representations. Finally, the classification scores from two streams are fused for action recognition. We conduct extensive experiments on four challenging action recognition datasets. The experimental results verify the effectiveness and superiority of our proposed method.