To date, the widely-adopted way to perform fixation collection in panoptic
video is based on a head-mounted display (HMD), where participants' 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
participants 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 of
complex panoramic scenes. This paper introduces the auxiliary Window with a
Dynamic Blurring (WinDB) fixation collection approach for panoptic video, which
doesn't need HMD and is blind-zoom-free. Thus, the collected fixations can 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. Besides, we have presented a simple baseline
design to take full advantage of PanopticVideo-300 to handle the
blind-zoom-free attribute-induced fixation shifting problem