  To date, the widely adopted way to perform fixation collection in panoptic
video is based on a head-mounted display (HMD), where users' fixations are
collected while wearing an HMD to explore the given panoptic scene freely.
However, this widely-used data collection method is insufficient for training
deep models to accurately predict which regions in a given panoptic are most
important when it contains intermittent salient events. The main reason is that
there always exist "blind zooms" when using HMD to collect fixations since the
users cannot keep spinning their heads to explore the entire panoptic scene all
the time. Consequently, the collected fixations tend to be trapped in some
local views, leaving the remaining areas to be the "blind zooms". Therefore,
fixation data collected using HMD-based methods that accumulate local views
cannot accurately represent the overall global importance - the main purpose of
fixations - of complex panoptic scenes. To conquer, this paper introduces the
auxiliary window with a dynamic blurring (WinDB) fixation collection approach
for panoptic video, which doesn't need HMD and is able to well reflect the
regional-wise importance degree. Using our WinDB approach, we have released a
new PanopticVideo-300 dataset, containing 300 panoptic clips covering over 225
categories. Specifically, since using WinDB to collect fixations is blind zoom
free, there exists frequent and intensive "fixation shifting" - a very special
phenomenon that has long been overlooked by the previous research - in our new
set. Thus, we present an effective fixation shifting network (FishNet) to
conquer it. All these new fixation collection tool, dataset, and network could
be very potential to open a new age for fixation-related research and
applications in 360o environments.
