Many of the state-of-the-art algorithms for gesture recognition are based on Conditional Random Fields (CRFs). Successful approaches, such as the Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose values are mapped to the values of the labels. In this paper we propose a novel methodology to set the latent values according to the gesture complexity. We use an heuristic that iterates through the samples associated with each label value, stimating their complexity. We then use it to assign the latent values to the label values. We evaluate our method on the task of recognizing human gestures from video streams. The experiments were performed in binary datasets, generated by grouping different labels. Our results demonstrate that our approach outperforms the arbitrary one in many cases, increasing the accuracy by up to 10 .
Hu and Sung also presented two-layered discriminative models for activity recognition. The model presented by Hu recognizes sub-level activities and high-level activities successively, using the assumption that a high-level activity is composed of multiple sub-activities. While the first layer of the hierarchical model predicts low-level activity for each temporal segment, the second uses the sub-activity to estimate the high-level activity @cite_6 . They added a set of latent variables to enrich the expressiveness of the second layer, and use the sub-activity as an observed variable. The model presented by Sung learns the sub-activities implicitly, by considering them as latent variables @cite_10 .
@cite_10 @cite_6
2017695267 2106169946
Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and point-cloud information. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM), which considers a person's activity as composed of a set of sub-activities. We infer the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve good performance even when the person was not seen before in the training set.1 Automated human activity recognition is an essential task for Human Robot Interaction (HRI). A successful activity recognition system enables an assistant robot to provide precise services. In this paper, we present a two-layered approach that can recognize sub-level activities and high-level activities successively. In the first layer, the low-level activities are recognized based on the RGB-D video. In the second layer, we use the recognized low-level activities as input features for estimating high-level activities. Our model is embedded with a latent node, so that it can capture a richer class of sub-level semantics compared with the traditional approach. Our model is evaluated on a challenging benchmark dataset. We show that the proposed approach outperforms the single-layered approach, suggesting that the hierarchical nature of the model is able to better explain the observed data. The results also show that our model outperforms the state-of-the-art approach in accuracy, precision and recall.