Pooja Viswanathan

Automated Spatial-Semantic Modeling with Applications to Place Labeling and Informed Search

This paper presents a spatial-semantic modeling system featuringautomated learning of object-place relations from an online annotateddatabase, and the application of these relations to a variety ofreal-world tasks. The system is able to label novel scenes with placeinformation, as we demonstrate on test scenes drawn from the same sourceas our training set. We have designed our system for future enhancementof a robot platform that performs state-of-the-art object recognitionand creates object maps of realistic environments. In this context, wedemonstrate the use of spatial-semantic information to performclustering and place labeling of object maps obtained from real homes.This place information is fed back into the robot system to inform anobject search planner about likely locations of a query object. As awhole, this system represents a new level in spatial reasoning andsemantic understanding for a physical platform.

Navigation and obstacle avoidance help (NOAH) for older adults with cognitive impairment: a pilot study

Many older adults with cognitive impairment are excluded from powered wheelchair use because of safety concerns. This leads to reduced mobility, and in turn, higher dependence on caregivers. In this paper, we describe an intelligent wheelchair that uses computer vision and machine learning methods to provide adaptive navigation assistance to users with cognitive impairment. We demonstrate the performance of the system in a user study with the target population. We show that the collision avoidance module of the system successfully decreases the number of collisions for all participants. We also show that the wayfinding module assists users with memory and vision impairments. We share feedback from the users on various aspects of the intelligent wheelchair system. In addition, we provide our own observations and insights on the target population and their use of intelligent wheelchairs. Finally, we suggest directions for future work.

Curious George: An Integrated Visual Search Platform

This paper describes an integrated robot system, known as Curious George, that has demonstrated state-of-the-art capabilities to recognize objects in the real world. We describe the capabilities of this system, including: the ability to access web-based training data automatically and in near real-time, the ability to model the visual appearance and 3D shape of a wide variety of object categories, navigation abilities such as exploration, mapping and path following, the ability to decompose the environment based on 3D structure, allowing for attention to be focused on regions of interest, the ability to capture high-quality images of objects in the environment, and finally, the ability to correctly label those objects with high accuracy. The competence of the combined system has been validated by entry into an international competition where Curious George has been among the top performing systems each year. We discuss the implications of such successful object recognition for society, and provide several avenues for potential improvement.

Place Classification Using Visual Object Categorization and Global Information

Places in an environment are locations where activities occur, and can be described by the objects they contain. This paper discusses the completely automated integration of object detection and global image properties for place classification. We first determine object counts in various place types based on Label Me images, which contain annotations of places and segmented objects. We then train object detectors on some of the most frequently occurring objects. Finally, we use object detection scores as well as global image properties to perform place classification of images. We show that our object-centric method is superior and more generalizable when compared to using global properties in indoor scenes. In addition, we show enhanced performance by combining both methods. We also discuss areas for improvement and the application of this work to informed visual search. Finally, through this work we display the performance of a state-of-the-art technique trained using automatically-acquired labeled object instances (i.e., bounding boxes) to perform place classification of realistic indoor scenes.

Automated Place Classification Using Object Detection

Places in an environment can be described by the objects they contain. This paper discusses the completely automated integration of object detection and place classification in a single system. We first perform automated learning of object-place relations from an online annotated database. We then train object detectors on some of the most frequently occurring objects. Finally, we use detection scores as well as learned object-place relations to perform place classification of images. We also discuss areas for improvement and the application of this work to informed visual search. As a whole, the system demonstrates the automated acquisition of training data containing labeled instances (i.e. bounding boxes) and the performance of a state-of-the-art object detection technique trained on this data to perform place classification of realistic indoor scenes.

Evaluation of the Navigation and Obstacle Avoidance Help (NOAH) system for wheelchair users with cognitive impairment

Powered wheelchairs are usually prescribed to older adults who are unable to propel themselves on manual wheelchairs. However, users with dementia often do not possess the reasoning and decision-making skills required for safe powered wheelchair operation. They are therefore excluded from powered wheelchair use and have to rely on caregivers to porter them around. This reduced mobility and independence can, in turn, lead to depression and social isolation. An intelligent powered wheelchair (NOAH) is proposed to restore independent mobility, while ensuring safety. Wayfinding assistance is also provided to ensure timely navigation. A stereovision camera and laptop are added on to a commercial powered wheelchair. Upon the detection of an imminent collision, the wheelchair is stopped, and motion towards the obstacle is prevented. Wayfinding assistance to the goal is provided by determining the wheelchair’s location using visual landmarks detected in camera images. The optimal route to the goal is constructed using existing path planning techniques. Adaptive audio navigation prompts that account for the users’ cognitive state are then issued using a probabilistic user model. The system is tested with older adults with mild-to-moderate cognitive impairment through a single-subject research design. Results demonstrate the high diversity of the target population, and highlight the need for customizable assistive technologies that account for the varying capabilities and requirements of the intended users. The collision avoidance module is able to improve safety for all users by lowering the number of frontal collisions. The wayfinding module assists users in navigating along shorter routes to the destination. Prompting accuracy is found to be high during the study. While compliance with correct prompts is high across all users, a distinct difference is found in the rates of compliance with incorrect prompts. Specifically, users who are unsure about the optimal route rely more highly on all system prompts for assistance, and are thus able to improve their wayfinding performance by following correct prompts. Improvements in wheelchair position estimation accuracy and joystick usability can help improve user performance and satisfaction. Further user studies can help refine user needs and hopefully allow us to increase the mobility and independence of several cognitively-impaired older adults. Pooja Viswanathan 2366 Main Mall Vancouver, BC V6T1Z4 poojav@cs.ubc.ca Pooja Viswanathan is currently a Ph.D. Candidate in Computer Science (Artificial Intelligence) at the University of British Columbia. She graduated from the University of Waterloo with a Bachelors of Math (Honours Computer Science) degree. Her research is focused on smart wheelchair prototype development using stereo-vision/artificial intelligence techniques that prevent collisions and provide adaptive audio prompts to drivers with cognitive impairment. James Little little@cs.ubc.ca Alan Mackworth mack@s.ubc.ca Alex Mihailidis alex.mihailidis@utoronto.ca