HTGSOM: Dynamic Selforganizing Map with Transience for Human Activity Recognition Recognition of complex human activities is a prominent area of research in intelligent video surveillance. The current stateoftheart techniques are largely based on supervised deep learning algorithms. The inability to learn from unlabeled video streams is a key shortcoming in supervised techniques in most current applications where large volumes of unlabeled video data are utilized. Furthermore, the dominant focus on persistence in traditional machine learning algorithms has induced two limitations; the influence of outdated information in memory guided decision making, and overfitting of acquired knowledge on specific past events, weakening the plasticity of the learning system. To address the above requirements, we propose a new adaptation of the Growing Self Organizing Map (GSOM), formed in a hierarchical twostream learning pipeline to accommodate unlabeled video data for human activity recognition, which facilitates plasticity by implementing a transience property, without losing the stability of the learning system. The proposed model is evaluated using two benchmark video datasets, confirming its validity and usability for human activity recognition.

Action Recognition Based on 3D Skeleton and RGB Frame Fusion Action recognition has wide applications in assisted living, health monitoring, surveillance, and humancomputer interaction. In traditional action recognition methods, RGB videobased ones are effective but computationally inefficient, while skeletonbased ones are computationally efficient but do not make use of lowlevel detail information. This work considers action recognition based on a multimodal fusion between the 3D skeleton and the RGB image. We design a neural network that uses a 3D skeleton sequence and a single middle frame from an RGB video as input. Specifically, our method picks up one frame in a video and extracts spatial features from it using two attention modules, a selfattention module and a skeletonattention module. Further, temporal features are extracted from the skeleton sequence via a BILSTM subnetwork. Finally, the spatial features and the temporal features are combined via a feature fusion network for action classification. A distinct feature of our method is that it uses only a single RGB frame rather than an RGB video. Accordingly, it has a lightweighted architecture and is more efficient than RGB videobased methods. Comparative evaluation on two public datasets, NTURGBD and SYSU, demonstrates that, our method can achieve competitive performance compared with stateoftheart methods.