Empirically Measuring Salience of Objects for Use in Pedestrian Navigation

Humans usually refer to landmarks when they give route directions to pedestrians. One of the reasons why current mobile pedestrian navigation systems do not yet mimic this mode of communication is the lack of available data sources. The usefulness of a crowd-sourced data acquisition approach to overcome this problem has long been mooted. However, to date no empirically sound way of measuring the salience of objects by means of surveys exists. GOAL Given this background, this doctoral work has three goals: 1. To achieve a sound way of measuring salience and its subdimensions, i.e. visibility in advance, cognitive salience, prototypicality, structural salience, and visual salience based on taking dimensions revealed in earlier studies systematically and simultaneously into account. 2. To find subgroups of visual features among the large number of visual attributes known from the literature. 3. To find the most important subdimensions of salience by means of estimating two different structural equation models. Model I is based on assumptions of independence among subdimensions, whereas model II reflects hypotheses of mediation. Taken as a whole, achieving these goals will foster both, the advancement of theories of salience and landmark acquisition methods. METHODOLOGY A large scale, in-situ experiment was implemented, trying to overcome weaknesses of earlier attempts made to estimate salience. An appropriate sample size of buildings and non-buildings was calculated a priori (nobj = 360). Objects were randomly selected based on their geographical coordinates and randomly grouped into nr = 55 routes. Participants were required to rate objects by means of a survey. The questions were derived from empirical evidence found in earlier studies. Each route was walked by two different participants (n = 112), id est (i.e.) two ratings per object were collected for data analysis. FINDINGS Model I and model II were analyzed using PLS Path Modeling and consistent PLS Path Modeling, respectively. The measurement models proposed showed a good fit, although some weaknesses were identified for prototypicality and cognitive salience. Geometrical aspects as well as features like (visual) age turned out to have a stronger impact on visual salience than color. Model I did not yield reasonable structural model results based on consistent Partial Least Squares Path Modeling. Model II, however, showed that visual salience had a very high impact on visibility in advance which, in turn, heavily influenced structural salience. An analysis of the predictive capabilities of model II revealed important, but rather small effects. VALUE OF WORK This doctoral work adds to salience models as well as to its empirical, survey-based, in-situ measurement. The results of the mediation analysis as well as the predictive capabilities of model II suggest that important subdimensions of salience are missing in current theories. Emotional salience and familiarity are identified as two candidate constructs. The structural relationships found during the analysis of model II provide, in combination with the measurement model results, a sound basis to choose important features for surveys which are usable to gain crowd-sourced salience ratings. Furthermore, several important aspects for future studies are identified. These include heterogeneity analyses for different subgroups of users of pedestrian navigation systems as well as local environments different to the historic one used in this study

DYNAMICS OF COLLABORATIVE NAVIGATION AND APPLYING DATA DRIVEN METHODS TO IMPROVE PEDESTRIAN NAVIGATION INSTRUCTIONS AT DECISION POINTS FOR PEOPLE OF VARYING SPATIAL APTITUDES

Cognitive Geography seeks to understand individual decision-making variations based on fundamental cognitive differences between people of varying spatial aptitudes. Understanding fundamental behavioral discrepancies among individuals is an important step to improve navigation algorithms and the overall travel experience. Contemporary navigation aids, although helpful in providing turn-by-turn directions, lack important capabilities to distinguish decision points for their features and importance. Existing systems lack the ability to generate landmark or decision point based instructions using real-time or crowd sourced data. Systems cannot customize personalized instructions for individuals based on inherent spatial ability, travel history, or situations. This dissertation presents a novel experimental setup to examine simultaneous wayfinding behavior for people of varying spatial abilities. This study reveals discrepancies in the information processing, landmark preference and spatial information communication among groups possessing differing abilities. Empirical data is used to validate computational salience techniques that endeavor to predict the difficulty of decision point use from the structure of the routes. Outlink score and outflux score, two meta-algorithms that derive secondary scores from existing metrics of network analysis, are explored. These two algorithms approximate human cognitive variation in navigation by analyzing neighboring and directional effect properties of decision point nodes within a routing network. The results are validated by a human wayfinding experiment, results show that these metrics generally improve the prediction of errors. In addition, a model of personalized weighting for users\u27 characteristics is derived from a SVMrank machine learning method. Such a system can effectively rank decision point difficulty based on user behavior and derive weighted models for navigators that reflect their individual tendencies. The weights reflect certain characteristics of groups. Such models can serve as personal travel profiles, and potentially be used to complement sense-of-direction surveys in classifying wayfinders. A prototype with augmented instructions for pedestrian navigation is created and tested, with particular focus on investigating how augmented instructions at particular decision points affect spatial learning. The results demonstrate that survey knowledge acquisition is improved for people with low spatial ability while decreased for people of high spatial ability. Finally, contributions are summarized, conclusions are provided, and future implications are discussed

How Subdimensions of Salience Influence Each Other. Comparing Models Based on Empirical Data

Theories about salience of landmarks in GIScience have been evolving for about 15 years. This paper empirically analyses hypotheses about the way different subdimensions (visual, structural, and cognitive aspects, as well as prototypicality and visibility in advance) of salience have an impact on each other. The analysis is based on empirical data acquired by means of an in-situ survey (360 objects, 112 participants). It consists of two parts: First, a theory-based structural model is assessed using variance-based Structural Equation Modeling. The results achieved are, second, corroborated by a data-driven approach, i.e. a tree-augmented naive Bayesian network is learned. This network is used as a structural model input for further analyses. The results clearly indicate that the subdimensions of salience influence each other

Schematic Maps and Indoor Wayfinding

Schematic maps are often discussed as an adequate alternative of displaying wayfinding information compared to detailed map designs. However, these depictions have not yet been compared and analyzed in-depth. In this paper, we present a user study that evaluates the wayfinding behaviour of participants either using a detailed floor plan or a schematic map that only shows the route to follow and landmarks. The study was conducted in an indoor real-world scenario. The depictions were presented with the help of a mobile navigation system. We analyzed the time it took to understand the wayfinding instruction and the workload of the users. Moreover, we examined how the depictions were visually perceived with a mobile eye tracker. Results show that wayfinders who use the detailed map spend more visual attention on the instructions. Nevertheless, the depiction does not help to solve the task: they also needed more time to orient themselves. Regarding the workload and the wayfinding errors no differences were found

Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people’s familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance

URWalking: Indoor Navigation for Research and Daily Use

In this report, we present the project URWalking conducted at the University of Regensburg. We describe its major outcomes: Firstly, an indoor navigation system for pedestrians as a web application and as an Android app with position tracking of users in indoor and outdoor environments. Our implementation showcases that a variant of the A∗-algorithm by Ullmann (tengetriebene optimierung präferenzadaptiver fußwegrouten durch gebäudekomplexe https://epub.uni-regensburg.de/43697/, 2020) can handle the routing problem in large, levelled indoor environments efficiently. Secondly, the apps have been used in several studies for a deeper understanding of human wayfinding. We collected eye tracking and synchronized video data, think aloud protocols, and log data of users interacting with the apps. We applied state-of-the-art deep learning models for gaze tracking and automatic classification of landmarks. Our results indicate that even the most recent version of the YOLO image classifier by Redmon and Farhadi (olov3: An incremental improvement. arXiv, 2018) needs finetuning to recognize everyday objects in indoor environments. Furthermore, we provide empirical evidence that appropriate machine learning models are helpful to bridge behavioural data from users during wayfinding and conceptual models for the salience of objects and landmarks. However, simplistic models are insufficient to reasonably explain wayfinding behaviour in real time—an open issue in GeoAI. We conclude that the GeoAI community should collect more naturalistic log data of wayfinding activities in order to build efficient machine learning models capable of predicting user reactions to routing instructions and of explaining how humans integrate stimuli from the environment as essential information into routing instructions while solving wayfinding tasks. Such models form the basis for real-time wayfinding assistance

A Conceptual Model of Exploration Wayfinding: An Integrated Theoretical Framework and Computational Methodology

This thesis is an attempt to integrate contending cognitive approaches to modeling wayfinding behavior. The primary goal is to create a plausible model for exploration tasks within indoor environments. This conceptual model can be extended for practical applications in the design, planning, and Social sciences. Using empirical evidence a cognitive schema is designed that accounts for perceptual and behavioral preferences in pedestrian navigation. Using this created schema, as a guiding framework, the use of network analysis and space syntax act as a computational methods to simulate human exploration wayfinding in unfamiliar indoor environments. The conceptual model provided is then implemented in two ways. First of which is by updating an existing agent-based modeling software directly. The second means of deploying the model is using a spatial interaction model that distributed visual attraction and movement permeability across a graph-representation of building floor plans

Visual landmark sequence-based indoor localization

This paper presents a method that uses common objects as landmarks for smartphone-based indoor localization and navigation. First, a topological map marking relative positions of common objects such as doors, stairs and toilets is generated from floor plan. Second, a computer vision technique employing the latest deep learning technology has been developed for detecting common indoor objects from videos captured by smartphone. Third, second order Hidden Markov model is applied to match detected indoor landmark sequence to topological map. We use videos captured by users holding smartphones and walking through corridors of an office building to evaluate our method. The experiment shows that computer vision technique is able to accurately and reliably detect 10 classes of common indoor objects and that second order hidden Markov model can reliably match the detected landmark sequence with the topological map. This work demonstrates that computer vision and machine learning techniques can play a very useful role in developing smartphone-based indoor positioning applications