Self-Supervised Video Pose Representation Learning for Occlusion-Robust Action Recognition

Action recognition based on human pose has witnessed increasing attention due to its robustness to changes in appearances, environments, and view-points. Despite associated progress, one remaining challenge has to do with occlusion in real-world videos that hinders the visibility of all joints. Such occlusion impedes representation of such scenes by models that have been trained on full-body pose data, obtained in laboratory conditions with specific sensors. To address this, as a first contribution, we introduce OR-VPE, a novel video pose embedding network that is streamlined to learn an occlusion-robust representation for pose sequences in videos. In order to enable our embedding network to handle partially visible joints, we propose to incorporate a sub-graph data augmentation mechanism during training, which simulates occlusions, into a video pose encoder based on Graph Convolutional Networks (GCNs). As a second contribution, we apply a contrastive learning module to train the video pose representation in a self-supervised manner without the necessity of action annotations. This is achieved by maximizing the mutual information of the same pose sequence pruned into different spatio-temporal subgraphs. Experimental analyses show that compared to training the same encoder from scratch, our proposed OR-VPE, with pre-training on a large-scale dataset, NTU-RGB+D 120, improves the performance of the downstream action classification on Toyota Smarthome, N-UCLA and Penn Action datasets.