Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning
This work addresses human activity recognition for applications like surveillance or healthcare, but it is incremental as it builds on existing LUPI and HCRF methods.
The authors tackled the problem of human activity recognition with missing information during testing by proposing HCRF+, a novel method integrating learning using privileged information into a hidden conditional random field model, which demonstrated effectiveness on four challenging datasets compared to state-of-the-art in the LUPI framework.
In this work, a novel method based on the learning using privileged information (LUPI) paradigm for recognizing complex human activities is proposed that handles missing information during testing. We present a supervised probabilistic approach that integrates LUPI into a hidden conditional random field (HCRF) model. The proposed model is called HCRF+ and may be trained using both maximum likelihood and maximum margin approaches. It employs a self-training technique for automatic estimation of the regularization parameters of the objective functions. Moreover, the method provides robustness to outliers (such as noise or missing data) by modeling the conditional distribution of the privileged information by a Student's \textit{t}-density function, which is naturally integrated into the HCRF+ framework. Different forms of privileged information were investigated. The proposed method was evaluated using four challenging publicly available datasets and the experimental results demonstrate its effectiveness with respect to the-state-of-the-art in the LUPI framework using both hand-crafted features and features extracted from a convolutional neural network.