Understanding Head-Mounted Display FOV in Maritime Search and Rescue Object Detection

Object detection when viewing Head Mounted Display (HMD) imagery for maritime Search and Rescue (SAR) detection tasks poses many challenges, for example, objects are difficult to distinguish due to low contrast or low observability. We survey existing Artificial Intelligence (AI) image processing algorithms that improve object detection performance. We also examine central and peripheral vision (HVS) and their relation to Field of View (FOV) within the Human Visual System when viewing such images using HMDs. We present results from our user-study which simulates different maritime scenes used in object detection tasks. Users are tested viewing sample images with different visual features over different FOVs, to inform the development of an AI algorithm for object detection.

[1]  Harold E. Bedell,et al.  The Effect of Flicker on Foveal and Peripheral Thresholds for Oscillatory Motion , 1995, Vision Research.

[2]  E. Peli,et al.  Image enhancement for the visually impaired. Simulations and experimental results. , 1991, Investigative ophthalmology & visual science.

[3]  Peter K Kaiser,et al.  Prospective evaluation of visual acuity assessment: a comparison of snellen versus ETDRS charts in clinical practice (An AOS Thesis). , 2009, Transactions of the American Ophthalmological Society.

[4]  Nelson F. F. Ebecken,et al.  A SURVEY ON VIDEO DETECTION AND TRACKING OF MARITIME VESSELS , 2014 .

[5]  Miguel P. Eckstein,et al.  Object detection through search with a foveated visual system , 2014, PLoS Comput. Biol..

[6]  Eli Peli,et al.  Augmented Vision for Central Scotoma and Peripheral Field Loss , 1999 .

[7]  Barry T. Thomas,et al.  Head-Mounted Mobility Aid for Low Vision Using Scene Classification Techniques , 1998, Int. J. Virtual Real..

[8]  Byron J. Pierce,et al.  Perceptual Issues in the Use of Head-Mounted Visual Displays , 2006, Hum. Factors.

[9]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[10]  Artur Lugmayr,et al.  Free UX Testing Tool: The LudoVico UX Machine for Physiological Sensor Data Recording, Analysis, and Visualization for User Experience Design Experiments , 2016, SEACHI@CHI.

[11]  Miguel P. Eckstein,et al.  Foveal analysis and peripheral selection during active visual sampling , 2014, Proceedings of the National Academy of Sciences.

[12]  Doug A. Bowman,et al.  Quantifying the benefits of immersion for procedural training , 2008, IPT/EDT '08.

[13]  Daniel Mestre CAVE versus Head-Mounted Displays: Ongoing thoughts , 2017 .

[14]  Philip M. Gaughan,et al.  EXAMINATION OF THE EFFECT OF FIELD OF VIEW ON URBAN TARGET DETECTION , 2005 .

[15]  Artur Lugmayr,et al.  Cognitive big data: survey and review on big data research and its implications. What is really "new" in big data? , 2017, J. Knowl. Manag..

[16]  Eric D. Ragan,et al.  Effects of Field of View and Visual Complexity on Virtual Reality Training Effectiveness for a Visual Scanning Task , 2015, IEEE Transactions on Visualization and Computer Graphics.

[17]  Gang Luo,et al.  Applications of augmented‐vision head‐mounted systems in vision rehabilitation , 2007, Journal of the Society for Information Display.