  With the rapid development of depth sensor, more and more RGB-D videos could
be obtained. Identifying the foreground in RGB-D videos is a fundamental and
important task. However, the existing salient object detection (SOD) works only
focus on either static RGB-D images or RGB videos, ignoring the collaborating
of RGB-D and video information. In this paper, we first collect a new annotated
RGB-D video SOD (ViDSOD-100) dataset, which contains 100 videos within a total
of 9,362 frames, acquired from diverse natural scenes. All the frames in each
video are manually annotated to a high-quality saliency annotation. Moreover,
we propose a new baseline model, named attentive triple-fusion network
(ATF-Net), for RGB-D video salient object detection. Our method aggregates the
appearance information from an input RGB image, spatio-temporal information
from an estimated motion map, and the geometry information from the depth map
by devising three modality-specific branches and a multi-modality integration
branch. The modality-specific branches extract the representation of different
inputs, while the multi-modality integration branch combines the multi-level
modality-specific features by introducing the encoder feature aggregation (MEA)
modules and decoder feature aggregation (MDA) modules. The experimental
findings conducted on both our newly introduced ViDSOD-100 dataset and the
well-established DAVSOD dataset highlight the superior performance of the
proposed ATF-Net. This performance enhancement is demonstrated both
quantitatively and qualitatively, surpassing the capabilities of current
state-of-the-art techniques across various domains, including RGB-D saliency
detection, video saliency detection, and video object segmentation. Our data
and our code are available at github.com/jhl-Det/RGBD_Video_SOD.
