Author:
Zhou Tao,Fan Deng-Ping,Cheng Ming-Ming,Shen Jianbing,Shao Ling
Abstract
AbstractSalient 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.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition
Reference205 articles.
1. Lecture Notes in Computer Science;D P Fan,2018
2. Nie, G.-Y.; Cheng, M.-M.; Liu, Y.; Liang, Z.; Fan, D.-P.; Liu, Y.; Wang, Y. Multi-level context ultra-aggregation for stereo matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3278–3286, 2019.
3. Zhu, J. Y.; Wu, J. J.; Xu, Y.; Chang, E., Tu, Z. W. Unsupervised object class discovery via saliency-guided multiple class learning. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 37, No. 4, 862–875, 2015.
4. Fan, D. P.; Li, T. P.; Lin, Z.; Ji, G. P.; Zhang, D. W.; Cheng, M. M.; Fu, H.; Shen, J. Re-thinking co-salient object detection. arXiv preprint arXiv:2007.03380, 2020.
5. Rapantzikos, K.; Avrithis, Y.; Kollias, S. Dense saliency-based spatiotemporal feature points for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1454–1461, 2009.
Cited by
182 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献