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AISN

ARD Presentation December, 2010. AISN. http://www.cs.bgu.ac.il/~royif/AISN. A uditory I maging for S ightless N avigation. Project Team. Academic Advisor: Dr. Yuval Elovici Technical Advisor : Dr. Rami Puzis Team Members: Yakir Dahan Royi Freifeld Vitali Sepetnitsky.

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AISN

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  1. ARD Presentation December, 2010 AISN http://www.cs.bgu.ac.il/~royif/AISN Auditory Imaging for Sightless Navigation

  2. Project Team Academic Advisor: Dr. Yuval Elovici Technical Advisor: Dr. Rami Puzis Team Members: YakirDahan RoyiFreifeld VitaliSepetnitsky

  3. Introduction

  4. The Problem Domain • Most of our navigation in the everyday life heavily depends on visual feedback that we get from our environment • When the ability to see the surroundings is missing due to visual impairments, the ability to navigate is also damaged

  5. Existing Solutions • Physical sense: • White Cane • Guide Dog • Sensory substitution: • Warning of obstacles (e.g. Optical Radar) • Sonar-like images scanning (e.g. The vOICe)

  6. Vision and Main Goals • Sightless navigation by sensory substitution • Development of an application that allows a person to navigate, relying primarily on the sense of hearing • Integration with a spatial auditory environment • Providing a flexible environment for future research

  7. Our Solution A Combination of visual information processing and 3D sound creation and positioning: • Taking a stream of frames from a web-camera • Processing the frames and retrieving visual information relevant to the user • Creating appropriate sounds according the recognized information • Performing an auditory spatialization of the sound and informing the user about the locations of the detected information

  8. Our Solution (cont.)

  9. External InterfacesHardware and Software • OpenCV • OpenAL • MATLAB engine library

  10. System Users • End Users • Visually impaired (or even blind) people who use the system for the purpose of hearing their physical environment • Configuration Users • The system installation and initial tuning, such as user profiles creation, will be done by configuration users having the ability to see the operations they perform • Researchers • Cognitive science researchers who wish to conduct experiments regarding 3D sound

  11. Functional Requirements

  12. Functional RequirementsCore Functionality • For all users (especially the researcher): • Support several types of computer vision and image processing algorithms for extraction of the following information: • Feature points (points of interest) • Contours • BLOBS (regions that are either darker or brighter than the surrounding) • Provide a utility to add new implementations of the above algorithms according to a predefined API • Support specific configurability options for each algorithm type

  13. Functional Requirements (cont.)Core Functionality (cont.) • For all users: • Create appropriate sounds according to the following features: • Location • Brightness • Color • Support sound spatialization using OpenAL API implementations and HRTF datasets conforming with a predefined format • Allow to install new HRTF datasets and OpenAL implementations for improving the quality of sound localization and research purposes

  14. Functional RequirementsOperational Functionality • For the configuration user: • Ability to install the system along with all the peripheral software and initial set of HRTF datasets • User profiles managing: • Support creation of user profiles, which store the system settings optimized to the user preferences • Support the ability to view the settings stored in a user profile • Support the ability to modify and delete profiles • Supply a set of predefined (default) profiles used for initial system configuring • Ability to initialize the system according to a given user profile and switch between profiles

  15. Functional RequirementsOperational Functionality (cont.) • For the blind user: • Support an extensive training mechanism for: • 3D sound perception • Environment understanding • Support the following training types: • Visualizing random shapes • Visualizing pre-defined image files • Fully immersive use of the system by emphasis of some feature • For the researcher: • Support defining a training experiment task • Support recording of the task results and retrieve them later

  16. Non FunctionalRequirements

  17. Non Functional Requirements Performance Constraints (partial) • Speed requirements: • Response time:The system will produce a 3D sound according to a frame taken by the camera within 0.1 seconds at most (we will strive to 0.03 seconds – 30 fps) • Training Speed: • A simple training in order to reach 50% accuracy of recognition should take no more than 30 minutes for a blind user . • A blind user should pass at least 80% of the accuracy tests after 2 days of extensive system usage. • A regular user should pass at least 80% of the accuracy tests after 3 days of extensive system usage.

  18. Non Functional Requirements (cont.) Performance Constraints (cont.) • Portability requirements: • Currently the system is designed to be deployed on Microsoft Windows (XP / Vista / 7 and later) operating systems only • The system will be compatible with 32 / 64 bit machines having web-camera and audio drivers installed • Capacity requirements: • The system should work on machines with at least 1 GB of RAM • The system will support many different OpenAL implementations and HRTF datasets, the limit is the hard disk capacity only

  19. Non Functional Requirements (cont.) Look, Feel and Use Constraints • User Interface requirements: • The UI should be easy to use even for users that are not well familiar to the computers technology • User interface will be in English • Documentation and Help: • A extensive documentation will be supported along with an installation guide • Operations will be implemented as wizards • Error messages heard via headphones

  20. Non Functional Requirements (cont.) SE Project + Platform Constraints • The application core and the UI will be written in C++ language using .NET 3.5 Framework and Visual Studio 10.0 IDE. • MATLAB will be used as a computational engine • During the development stage of the system a home-made simple device will be used (a PC web-camera strapped to a top of headphones) • For the demo and testing purposes, a real device will be supplied by DT labs which are spy-sunglasses (sunglasses with a tiny camera hidden in the nose bridge of glasses)

  21. Usage Scenarios

  22. Usage Scenarios Use Cases Diagram

  23. Usage Scenarios (cont.) Use Cases: UC-1: Visualize Environment A blind user starts the visualization process

  24. Usage Scenarios (cont.) Use Cases: UC-2: Train A blind user performs a training process

  25. Usage Scenarios (cont.) Use Cases: UC-3: Choose a user profile A blind user chooses an existing user profile for the purpose of performing a training or in order to use the system

  26. Usage Scenarios (cont.) Use Cases: UC-4: Visualize Image The core of the visualization process

  27. Usage Scenarios (cont.)UC-4: Visualize Image

  28. Questions ? Thank You!

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