Scene Depth Estimation Based on Monocular Vision in Advanced Driving Assistance System

Meng Ding; Xinyan Jiang

doi:10.3788/AOS202040.1715001

[1] Ranft B, Stiller C. The role of machine vision for intelligent vehicles[J]. IEEE Transactions on Intelligent Vehicles, 1, 8-19(2016).

[2] Bengler K, Dietmayer K, Farber B et al. Three decades of driver assistance systems: review and future perspectives[J]. IEEE Intelligent Transportation Systems Magazine, 6, 6-22(2014).

[3] Zaklouta F, Stanciulescu B. Real-time traffic-sign recognition using tree classifiers[J]. IEEE Transactions on Intelligent Transportation Systems, 13, 1507-1514(2012).

[4] Arnold E. Al-Jarrah O Y, Dianati M, et al. A survey on 3D object detection methods for autonomous driving applications[J]. IEEE Transactions on Intelligent Transportation Systems, 20, 3782-3795(2019).

[5] Ding M, Zhang X, Chen W H et al. Thermal infrared pedestrian tracking via fusion of features in driving assistance system of intelligent vehicles[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233, 6089-6103(2019).

[6] Zheng T X, Huang S, Li Y F et al. Key techniques for vision based 3D reconstruction: a review[J]. Acta Automatica Sinica, 46, 631-652(2020).

[7] Liu X M, Du M Z, Ma Z B et al. Depth estimation method of light field image based on occlusion scene[J]. Acta Optica Sinica, 40, 0510002(2020).

[8] Wu X W, Sahoo D. Hoi S C H. Recent advances in deep learning for object detection[J]. Neurocomputing, 396, 39-64(2020).

[9] Chang L, Deng X M, Zhou M Q et al. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica, 42, 1300-1312(2016).

[10] Meng L, Yang X. A survey of object tracking algorithms[J]. Acta Automatica Sinica, 45, 1244-1260(2019).

[11] Li P X, Wang D, Wang L J et al. Deep visual tracking: review and experimental comparison[J]. Pattern Recognition, 76, 323-338(2018).

[12] Huang J, Wang C, Liu Y et al. The progress of monocular depth estimation technology[J]. Journal of Image and Graphics, 24, 2081-2097(2019).

[13] Eigen D, Puhrsch C. -6-09)[2020-04-25]. https:∥arxiv., org/abs/1406, 2283(2014).

[14] Eigen D, Fergus R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2650-2658(2015).

[15] Liu F Y, Shen C H, Lin G S et al. Learning depth from single monocular images using deep convolutional neural fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2024-2039(2016).

[16] Li B, Shen C H, Dai Y C et al. Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 1119-1127(2015).

[17] Zhou T H, Brown M, Snavely N et al. Unsupervised learning of depth and ego-motion from video. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 6612-6619(2017).

[18] Xie J Y, Girshick R, Farhadi A. Deep3D: fully automatic 2D-to-3D video conversion with deep convolutional neural networks[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Lecture Notes in Computer Science. Cham: Springer, 9908, 842-857(2016).

[19] Garg R. Vijay K B G, Carneiro G, et al. Unsupervised CNN for single view depth estimation: geometry to the rescue[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Lecture Notes in Computer Science. Cham: Springer, 9912, 740-756(2016).

[20] Godard C, Aodha O M, Brostow G J. Unsupervised monocular depth estimation with left-right consistency. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 6602-6611(2017).

[21] Wang S, Xu X. 3D reconstruction based on horopter[J]. Acta Optica Sinica, 37, 0515004(2017).

[22] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 13, 600-612(2004).

[23] Zhao H, Gallo O, Frosio I et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 3, 47-57(2017).

[24] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[25] Jiang X Y, Ding M. Unsupervised monocular depth estimation with scale unification. [C]∥2019 12th International Symposium on Computational Intelligence and Design (ISCID), December 14-15, 2019, Hangzhou, China. New York: IEEE, 284-287(2019).