Abstract
Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.
Highlights
1.1 BackgroundSalient object detection aims to locate the most visually prominent object(s) in a given scene [1].It plays a key role in a range of real-world applications, such as stereo matching [2], image understanding [3], co-saliency detection [4], action recognition [5], video detection and segmentation [6–9], semantic segmentation [10, 11], medical image segmentation [12–14], object tracking [15, 16], person re-identification [17, 18], camouflaged object detection [19], image retrieval [20], etc
Unlike previous salient object detection surveys, in this paper, we focus on reviewing RGB-D based salient object detection models and benchmark datasets
This paper has presented the first comprehensive review of RGB-D based salient object detection models
Summary
1.1 BackgroundSalient object detection aims to locate the most visually prominent object(s) in a given scene [1].It plays a key role in a range of real-world applications, such as stereo matching [2], image understanding [3], co-saliency detection [4], action recognition [5], video detection and segmentation [6–9], semantic segmentation [10, 11], medical image segmentation [12–14], object tracking [15, 16], person re-identification [17, 18], camouflaged object detection [19], image retrieval [20], etc. Inaccurate or low-quality depth maps often decrease performance To overcome this issue, light field salient object detection methods have been proposed to make use of the rich information captured by a light field. LHM [51] ACSD [56] DESM [49] GP [50] LBE [57] DCMC [36] SE [37] CDCP [84] CDB [95] DF [52] PCF [92] CTMF [58] CPFP [53] TANet [103] AFNet [106] MMCI [55] DMRA [54] D3Net [38] SSF [39] A2dele [40] S2MA [41] ICNet [42] JL-DCF [43] UC-Net [44] Scale MAE Sα In this evaluation, we utilize five traditional salient object detection methods: BSCA [161], CLC [162], MDC [163], MIL [164], and WFD [165], to first detect salient objects in various images, and categorise these images as having simple or complex backgrounds according to the results. The four most recent models, i.e., D3Net [38], S2MA [41], A2dele [40], and ICNet [42] obtain better performance than the others
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