http://dl.gi.de/handle/20.500.12116/11029 Thu, 23 Jul 2026 04:33:53 GMT 2026-07-23T04:33:53Z http://dl.gi.de/handle/20.500.12116/11077 Automatic Detection of Visual Search for the Elderly using Eye and Head Tracking Data Dietz, Michael; Schork, Daniel; Damian, Ionut; Steinert, Anika; Haesner, Marten; André, Elisabeth With increasing age we often find ourselves in situations where we search for certain items, such as keys or wallets, but cannot remember where we left them before. Since finding these objects usually results in a lengthy and frustrating process, we propose an approach for the automatic detection of visual search for older adults to identify the point in time when the users need assistance. In order to collect the necessary sensor data for the recognition of visual search, we develop a completely mobile eye and head tracking device specifically tailored to the requirements of older adults. Using this device, we conduct a user study with 30 participants aged between 65 and 80 years ($$avg = 71.7,$$avg=71.7, 50% female) to collect training and test data. During the study, each participant is asked to perform several activities including the visual search for objects in a real-world setting. We use the recorded data to train a support vector machine (SVM) classifier and achieve a recognition rate of 97.55% with the leave-one-user-out evaluation method. The results indicate the feasibility of an approach towards the automatic detection of visual search in the wild. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11077 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11082 Red Hen Lab: Dataset and Tools for Multimodal Human Communication Research Joo, Jungseock; Steen, Francis F.; Turner, Mark Researchers in the fields of AI and Communication both study human communication, but despite the opportunities for collaboration, they rarely interact. Red Hen Lab is dedicated to bringing them together for research on multimodal communication, using multidisciplinary teams working on vast ecologically-valid datasets. This article introduces Red Hen Lab with some possibilities for collaboration, demonstrating the utility of a variety of machine learning and AI-based tools and methods to fundamental research questions in multimodal human communication. Supplemental materials are at http://babylon.library.ucla.edu/redhen/KI. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11082 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11078 AI, Kitsch, and Communication Hertzberg, Joachim Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11078 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11076 Declarative Reasoning about Space and Motion with Video Suchan, Jakob We present a commonsense theory of space and motion for representing and reasoning about motion patterns in video data, to perform declarative (deep) semantic interpretation of visuo-spatial sensor data, e.g., coming from object tracking, eye tracking data, movement trajectories. The theory has been implemented within constraint logic programming to support integration into large scale AI projects. The theory is domain independent and has been applied in a range of domains, in which the capability to semantically interpret motion in visuo-spatial data is central. In this paper, we demonstrate its capabilities in the context of cognitive film studies for analysing visual perception of spectators by integrating the visual structure of a scene and spectators gaze acquired from eye tracking experiments. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11076 2017-01-01T00:00:00Z