# ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection Source: [ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:40:00.946Z Authors: Junhao Lin, Lei Zhu, Jiaxing Shen, Huazhu Fu, Qing Zhang, Liansheng Wang Published: 2024-06-18T12:09:43.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Video Salient Object Detection on RDVS](): Video Salient Object Detection; RDVS; Sm: 0.741. - [Video Salient Object Detection on ViDSOD](): Video Salient Object Detection; ViDSOD; Sm: 87.5. - [Video Salient Object Detection on DVisal](): Video Salient Object Detection; DVisal; Sm: 72.3. - [RGB-D Video Salient Object Detection on ViDSOD-100](): RGB-D Video Salient Object Detection; ViDSOD-100; S\_alpha: 87.5. - [RGB-D Video Salient Object Detection on DViSal](): RGB-D Video Salient Object Detection; DVisal; S\_alpha: 72.4. - [RGB-D Video Salient Object Detection on RDVS](): RGB-D Video Salient Object Detection; RDVS; S\_alpha: 71.3. ## Original sources and implementation [Original paper]() [PDF]()