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. Us-ing 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 ave-nue 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 re-quired to determine warning frequency and modality to optimize the balance be-tween saliency and workload increase. Future work will focus on adding a real-time vigilance detection and countermeasure capability

See no Evil: Challenges of security surveillance and monitoring

While the development of intelligent technologies in security surveillance can augment human capabilities, they do not replace the role of the operator entirely; as such, when developing surveillance support it is critical that limitations to the cognitive system are taken into account. The current article reviews the cognitive challenges associated with the task of a CCTV operator: visual search and cognitive/perceptual overload, attentional failures, vulnerability to distraction, and decision-making in a dynamically evolving environment. While not directly applied to surveillance issues, we suggest that the NSEEV (noticing – salience, effort, expectancy, value) model of attention could provide a useful theoretical basis for understanding the challenges faced in detection and monitoring tasks. Having identified cognitive limitations of the human operator, this review sets out a research agenda for further understanding the cognitive functioning related to surveillance, and highlights the need to consider the human element at the design stage when developing technological solutions to security surveillance

Modeling and Evaluating Pilot Performance in NextGen: Review of and Recommendations Regarding Pilot Modeling Efforts, Architectures, and Validation Studies

NextGen operations are associated with a variety of changes to the national airspace system (NAS) including changes to the allocation of roles and responsibilities among operators and automation, the use of new technologies and automation, additional information presented on the flight deck, and the entire concept of operations (ConOps). In the transition to NextGen airspace, aviation and air operations designers need to consider the implications of design or system changes on human performance and the potential for error. To ensure continued safety of the NAS, it will be necessary for researchers to evaluate design concepts and potential NextGen scenarios well before implementation. One approach for such evaluations is through human performance modeling. Human performance models (HPMs) provide effective tools for predicting and evaluating operator performance in systems. HPMs offer significant advantages over empirical, human-in-the-loop testing in that (1) they allow detailed analyses of systems that have not yet been built, (2) they offer great flexibility for extensive data collection, (3) they do not require experimental participants, and thus can offer cost and time savings. HPMs differ in their ability to predict performance and safety with NextGen procedures, equipment and ConOps. Models also vary in terms of how they approach human performance (e.g., some focus on cognitive processing, others focus on discrete tasks performed by a human, while others consider perceptual processes), and in terms of their associated validation efforts. The objectives of this research effort were to support the Federal Aviation Administration (FAA) in identifying HPMs that are appropriate for predicting pilot performance in NextGen operations, to provide guidance on how to evaluate the quality of different models, and to identify gaps in pilot performance modeling research, that could guide future research opportunities. This research effort is intended to help the FAA evaluate pilot modeling efforts and select the appropriate tools for future modeling efforts to predict pilot performance in NextGen operations