A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition
This work addresses the challenge of recognizing diverse human activities from 3D/depth sensor data, which is important for applications like surveillance and human-computer interaction, but it appears incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackles 3D human activity recognition by proposing a deep structured model that adaptively decomposes activities into temporal parts using CNNs and incorporates a radius-margin bound for regularization, achieving superior performance over state-of-the-art approaches in experiments on complex scenarios.
Understanding human activity is very challenging even with the recently developed 3D/depth sensors. To solve this problem, this work investigates a novel deep structured model, which adaptively decomposes an activity instance into temporal parts using the convolutional neural networks (CNNs). Our model advances the traditional deep learning approaches in two aspects. First, { we incorporate latent temporal structure into the deep model, accounting for large temporal variations of diverse human activities. In particular, we utilize the latent variables to decompose the input activity into a number of temporally segmented sub-activities, and accordingly feed them into the parts (i.e. sub-networks) of the deep architecture}. Second, we incorporate a radius-margin bound as a regularization term into our deep model, which effectively improves the generalization performance for classification. For model training, we propose a principled learning algorithm that iteratively (i) discovers the optimal latent variables (i.e. the ways of activity decomposition) for all training instances, (ii) { updates the classifiers} based on the generated features, and (iii) updates the parameters of multi-layer neural networks. In the experiments, our approach is validated on several complex scenarios for human activity recognition and demonstrates superior performances over other state-of-the-art approaches.