This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB-Depth-Thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian Mixture Models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75% on the novel dataset when compared to the manually annotated ground-truth of human segmentations.

We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras. The dataset, which is anonymized to remove any personally identifiable information on participants, consists of 80 participants of varied appearance performing several gaze-elicited tasks, and is divided in two subsets: 1) Gaze Prediction Dataset, with up to 66,560 sequences containing 550,400 eye-images and respective gaze vectors, created to foster research in spatio-temporal gaze estimation and prediction approaches; and 2) Eye Segmentation Dataset, consisting of 200 sequences sampled at 5 Hz, with up to 29,500 images, of which 5% contain a semantic segmentation label, devised to encourage the use of temporal information to propagate labels to contiguous frames. Baseline experiments have been evaluated on OpenEDS2020, one for each task, with average angular error of 5.37 degrees when performing gaze prediction on 1 to 5 frames into the future, and a mean intersection over union score of 84.1% for semantic segmentation. As its predecessor, OpenEDS dataset, we anticipate that this new dataset will continue creating opportunities to researchers in eye tracking, machine learning and computer vision communities, to advance the state of the art for virtual reality applications. The dataset is available for download upon request at http://research.fb.com/programs/ openeds-2020-challenge/.

The EMPATHIC project develops and validates new interaction paradigms for personalized virtual coaches (VC) to promote healthy and independent aging. To this end, the work presented in this paper is aimed to analyze the interaction between the EMPATHIC-VC and the users. One of the goals of the project is to ensure an end-user driven design, involving senior users from the beginning and during each phase of the project. Thus, the paper focuses on some sessions where the seniors carried out interactions with a Wizard of Oz driven, simulated system. A coaching strategy based on the GROW model was used throughout these sessions so as to guide interactions and engage the elderly with the goals of the project. In this interaction framework, both the human and the system behavior were analyzed. The way the wizard implements the GROW coaching strategy is a key aspect of the system behavior during the interaction. The language used by the virtual agent as well as his or her physical aspect are also important cues that were analyzed. Regarding the user behavior, the vocal communication provides information about the speaker’s emotional status, that is closely related to human behavior and which can be extracted from the speech and language analysis. In the same way, the analysis of the facial expression, gazes and gestures can provide information on the non verbal human communication even when the user is not talking. In addition, in order to engage senior users, their preferences and likes had to be considered. To this end, the effect of the VC on the users was gathered by means of direct questionnaires. These analyses have shown a positive and calm behavior of users when interacting with the simulated virtual coach as well as some difficulties of the system to develop the proposed coaching strategy.

This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures.

No abstract

No abstract

This paper summarizes the OpenEDS 2020 Challenge dataset, the proposed baselines, and results obtained by the top three winners of each competition: (1) Gaze prediction Challenge, with the goal of predicting the gaze vector 1 to 5 frames into the future based on a sequence of previous eye images, and (2) Sparse Temporal Semantic Segmentation Challenge, with the goal of using temporal information to propagate semantic eye labels to contiguous eye image frames. Both competitions were based on the OpenEDS2020 dataset, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display with two synchronized eye-facing cameras. The dataset, which we make publicly available for the research community, consists of 87 subjects performing several gaze-elicited tasks, and is divided into 2 subsets, one for each competition task. The proposed baselines, based on deep learning approaches, obtained an average angular error of 5.37 degrees for gaze prediction, and a mean intersection over union score (mIoU) of 84.1% for semantic segmentation. The winning solutions were able to outperform the baselines, obtaining up to 3.17 degrees for the former task and 95.2% mIoU for the latter.

No abstract