Gaze-Aware Cognitive Assistant for Multiscreen Surveillance
Surveillance operators must scan multiple camera feeds to ensure timely detection of incidents; however, variability in scanning behavior can lead to untimely/failed detection of critical information in feeds that were neglected for a long period. Using an eye tracker to monitor screen fixations we can calculate (in real-time) the time elapsed since the last scan of each particular feed, allowing the setting-up of targeted countermeasures contingent on operator oculomotor behavior. One avenue is to provide operators with timely alerts to modulate the scan pattern to avoid attentional tunneling and inattentional blindness. We test such an adaptive solution within a major event surveillance simulation and preliminary results show that operator scan behavior can be modulated, although further investigation is required to determine warning frequency and modality to optimize the balance between saliency and workload increase. Future work will focus on adding a real-time vigilance detection and countermeasure capability.
KeywordsSecurity surveillance Human-computer interaction Cognitive support Eye tracking Adaptive intelligent system
This work was supported by grants from the National Sciences and Engineering Research Council of Canada (NSERC), the Ministère de l’économie, des sciences et de l’innovation du Québec (MESI), and Prompt Information Technology R&D awarded to Sébastien Tremblay and François Vachon. We are also grateful to the financial and in-kind contributions of Thales Research and Technology Canada.
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