Ioannis Giannopoulos

Where Am I? Investigating Map Matching During Self-Localization With Mobile Eye Tracking in an Urban Environment

Self-localization is the process of identifying ones current position on a map, and it is a crucial part of any wayfinding process. During self-localization the wayfinder matches visually perceptible features of the environment, such as landmarks, with map symbols to constrain potential locations on the map. The success of this visual matching process constitutes an important factor for the success of self-localization. In this research we aim at observing the visual matching process between environment and map during self-localization with real-world mobile eye tracking. We report on one orientation and one self-localization experiment, both in an outdoor urban environment. The gaze data collected during the experiments show that successful participants put significantly more visual attention to those symbols on the map that were helpful in the given situation than unsuccessful participants. A sequence analysis revealed that they also had significantly more switches of visual attention between map symbols and their corresponding landmarks in the environment, which suggests they were following a more effective self-localization strategy.

GeoGazemarks: providing gaze history for the orientation on small display maps

Orientation on small display maps is often difficult because the visible spatial context is restricted. This paper proposes to provide the history of a users visual attention on a map as visual clue to facilitate orientation. Visual attention on the map is recorded with eye tracking, clustered geo-spatially, and visualized when the user zooms out. This implicit gaze-interaction concept, called GeoGazemarks, has been evaluated in an experiment with 40 participants. The study demonstrates a significant increase in efficiency and an increase in effectiveness for a map search task, compared to standard panning and zooming.

Using eye movements to recognize activities on cartographic maps

The spatio-temporal characteristics of eye movements vary according to the activity the user of a cartographic map is performing. In this paper, we use these eye movement characteristics to automatically detect the map users activity, an approach with great potential in gaze-assistive map interfaces. A dataset of 587 eye movement recordings from 17 participants was used to train and cross-validate a support vector machine (SVM) classifier over 229 features. The classifier can distinguish 6 common map activities with an accuracy of approx. 78%.

GazeNav: Gaze-Based Pedestrian Navigation

Pedestrian navigation systems help us make a series of decisions that lead us to a destination. Most current pedestrian navigation systems communicate using map-based turn-by-turn instructions. This interaction mode suffers from ambiguity, its users ability to match the instruction with the environment, and it requires a redirection of visual attention from the environment to the screen. In this paper we present GazeNav, a novel gaze-based approach for pedestrian navigation. GazeNav communicates the route to take based on the users gaze at a decision point. We evaluate GazeNav against the map-based turn-by-turn instructions. Based on an experiment conducted in a virtual environment with 32 participants we found a significantly improved user experience of GazeNav, compared to map-based instructions, and showed the effectiveness of GazeNav as well as evidence for better local spatial learning. We provide a complete comparison of navigation efficiency and effectiveness between the two approaches.

Gaze map matching: mapping eye tracking data to geographic vector features

This paper introduces gaze map matching as the problem of algorithmically interpreting eye tracking data with respect to geographic vector features, such as a road network shown on a map. This differs from previous eye tracking studies which have not taken into account the underlying vector data of the cartographic map. The paper explores the challenges of gaze map matching and relates it to the (vehicle) map matching problem. We propose a gaze map matching algorithm based on a Hidden Markov Model, and compare its performance with two purely geometric algorithms. Two eye tracking data sets recorded during the visual inspection of 14 road network maps of varying realism and complexity are used for this evaluation.

Eye tracking for spatial research: Cognition, computation, challenges

Spatial information acquisition happens in large part through the visual sense. Studying visual attention and its connection to cognitive processes has been the interest of many research efforts in spatial cognition over the years. Recent technological developments have led to an increasing popularity of eye-tracking methodology for investigating research questions related to spatial cognition, geographic information science (GIScience) and cartography. At the same time, eye trackers can nowadays be used as an input device for (cognitively engineered) user interfaces to geographic information. We provide an overview of the most recent literature advancing and utilizing eye-tracking methodology in these fields, introduce the research articles in this Special Issue, and discuss challenges and opportunities for future research.

Starting to get bored: an outdoor eye tracking study of tourists exploring a city panorama

Predicting the moment when a visual explorer of a place loses interest and starts to get bored is of considerable importance to the design of touristic information services. This paper investigates factors affecting the duration of the visual exploration of a city panorama. We report on an empirical outdoor eye tracking study in the real world with tourists following a free exploration paradigm without a time limit. As main result, the number of areas of interest revisited during a short period was found to be a good predictor for the total exploration duration.

Measuring Cognitive Load for Map Tasks Through Pupil Diameter

In this paper we use pupil diameter as an indicator for measuring cognitive load for six different tasks on common web maps. Two eye tracking data sets were collected for different basemaps (37 participants and 1,328 trials in total). We found significant differences in mean pupil diameter between tasks, indicating low cognitive load for free exploration, medium cognitive load for search, polygon comparison, line following, and high cognitive load for route planning and focused search. Pupil diameter also changed over time within trials which can be interpreted as an increase in cognitive load for search and focused search, and a decrease for line following. Such results can be used for the adaptation of maps and geovisualizations based on their users’ cognitive load.

Wayfinding Decision Situations: A Conceptual Model and Evaluation

Humans engage in wayfinding many times a day. We try to find our way in urban environments when walking towards our work places or when visiting a city as tourists. In order to reach the targeted destination, we have to make a series of wayfinding decisions of varying complexity. Previous research has focused on classifying the complexity of these wayfinding decisions, primarily looking at the complexity of the decision point itself (e.g., the number of possible routes or branches). In this paper, we proceed one step further by incorporating the user, instructions, and environmental factors into a model that assesses the complexity of a wayfinding decision. We constructed and evaluated three models using data collected from an outdoor wayfinding study. Our results suggest that additional factors approximate the complexity of a wayfinding decision better than the simple model using only the number of branches as a criterion.

Eye movement synthesis

A convolution-filtering technique is introduced for the synthesis of eye gaze data. Its purpose is to produce, in a controlled manner, a synthetic stream of raw gaze position coordinates, suitable for: (1) testing event detection filters, and (2) rendering synthetic eye movement animations for testing eye tracking gaze estimation algorithms. Synthetic gaze data is parameterized by sampling rate, microsaccadic jitter, and simulated measurement error. Sampled synthetic gaze data is compared against real data captured by an eye tracker showing similar signal characteristics.