A best view selection in meetings through attention analysis using a multi-camera network

Human activity analysis is an essential task in ambient intelligence and computer vision. The main focus lies in the automatic analysis of ongoing activities from a multi-camera network. One possible application is meeting analysis which explores the dynamics in meetings using low-level data and inferring high-level activities. However, the detection of such activities is still very challenging due to the often corrupted or imprecise low-level data. In this paper, we present an approach to understand the dynamics in meetings using a multi-camera network, consisting of fixed ambient and portable close-up cameras. As a particular application we are aiming to find the most informative video stream, for example as a representative view for a remote participant. Our contribution is threefold: at first, we estimate the extrinsic parameters of the portable close-up cameras based on head positions. Secondly, we find common overlapping areas based on the consensus of people’s orientation. And thirdly, the most informative view for a remote participant is estimated using common overlapping areas. We evaluated our proposed approach and compared it to a motion estimation method. Experimental results show that we can reach an accuracy of 74% compared to manually selected views

Healthcare Event and Activity Logging.

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean

Recent advances in video-based human action recognition using deep learning: A review

© 2017 IEEE. Video-based human action recognition has become one of the most popular research areas in the field of computer vision and pattern recognition in recent years. It has a wide variety of applications such as surveillance, robotics, health care, video searching and human-computer interaction. There are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. A large number of techniques have been proposed to address the challenges over the decades. Three different types of datasets namely, single viewpoint, multiple viewpoint and RGB-depth videos, are used for research. This paper presents a review of various state-of-the-art deep learning-based techniques proposed for human action recognition on the three types of datasets. In light of the growing popularity and the recent developments in video-based human action recognition, this review imparts details of current trends and potential directions for future work to assist researchers

Real-time action recognition using a multilayer descriptor with variable size

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Video analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveillance environments, such as forensic investigation, patient monitoring, medical training, accident prevention, and traffic monitoring, among others. We present a method for action identification based on adaptive training of a multilayer descriptor applied to a single classifier. Cumulative motion shapes (CMSs) are extracted according to the number of frames present in the video. Each CMS is employed as a self-sufficient layer in the training stage but belongs to the same descriptor. A robust classification is achieved through individual responses of classifiers for each layer, and the dominant result is used as a final outcome. Experiments are conducted on five public datasets (Weizmann, KTH, MuHAVi, IXMAS, and URADL) to demonstrate the effectiveness of the method in terms of accuracy in real time. (C) 2016 SPIE and IS&TVideo analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveill2501FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)SEM INFORMAÇÃOSEM INFORMAÇÃ

Visually Plausible Human-Object Interaction Capture from Wearable Sensors

In everyday lives, humans naturally modify the surrounding environmentthrough interactions, e.g., moving a chair to sit on it. To reproduce suchinteractions in virtual spaces (e.g., metaverse), we need to be able to captureand model them, including changes in the scene geometry, ideally fromego-centric input alone (head camera and body-worn inertial sensors). This isan extremely hard problem, especially since the object/scene might not bevisible from the head camera (e.g., a human not looking at a chair whilesitting down, or not looking at the door handle while opening a door). In thispaper, we present HOPS, the first method to capture interactions such asdragging objects and opening doors from ego-centric data alone. Central to ourmethod is reasoning about human-object interactions, allowing to track objectseven when they are not visible from the head camera. HOPS localizes andregisters both the human and the dynamic object in a pre-scanned static scene.HOPS is an important first step towards advanced AR/VR applications based onimmersive virtual universes, and can provide human-centric training data toteach machines to interact with their surroundings. The supplementary video,data, and code will be available on our project page athttp://virtualhumans.mpi-inf.mpg.de/hops/<br

A Data Fusion Perspective on Human Motion Analysis Including Multiple Camera Applications

Proceedings of: 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, (IWINAC 2013). Mallorca, Spain, June 10-14.Human motion analysis methods have received increasing attention during the last two decades. In parallel, data fusion technologies have emerged as a powerful tool for the estimation of properties of objects in the real world. This papers presents a view of human motion analysis from the viewpoint of data fusion. JDL process model and Dasarathy's input-output hierarchy are employed to categorize the works in the area. A survey of the literature in human motion analysis from multiple cameras is included. Future research directions in the area are identified after this review.Publicad