Abstract
This paper proposes a panoramic video stitching algorithm based on seam optimization, which aims at stitching two videos taken by two wide-angle cameras into a single 720-degree video. The use of only two cameras makes the parallax of the dual videos very large, while previous stitching methods based on deformation or seams incur problems like distortion, blur and ghost. To solve these problems, we improve the graph-cut algorithm to compute the optimal seams in the overlapped regions. For the spatial and temporal consistency of the panoramic video, foreground detection and Gaussian filter are employed to generate a sequence of smooth seams. Besides, a quantitative evaluation on the seam quality is proposed for the linear fusion of the stitched frames. Compared with previous methods, our work can effectively reduce the distortion, blur and ghost artifacts, as well as maintain good spatial and temporal consistency of the panoramic video as evidenced by the experiments.












Similar content being viewed by others
References
Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis & Machine Intelligence 26(9):1124–1137
Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73
Chang CH, Sato Y, Chuang YY (2014) Shape-preserving half-projective warps for image stitching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3254–3261
Chen T, Cheng MM, Tan P, Hu SM and et al. (2009) Sketch2photo: internet image montage. ACM Transactions on Graphics (TOG), pages 89–97
F P, A S-H, H Z and et al. (2015) Panoramic video from unstructured camera arrays. Computer Graphics Forum, pages 57–68
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
H B, T T, Surf GLV (2006) Speeded up robust features. Comput Vis Image Underst 110(3):404–417
H KC, C PY, C CA and et al. (2014) A 360-degree panoramic video system design. International Symposium on VLSI Design, Automation and Test, pages 1–4
He B, Yu S (2015) Parallax-robust surveillance video stitching. Sensors 16(1):1–7
Jia Q, Fan X, Liu Y et al (2016) Hierarchical projective invariant contexts for shape recognition. Pattern Recogn 52:358–374
K L, S L, L C et al (2016) Seamless video stitching from handheld camera inputs. Computer Graphics Forum 35(2):479–487
Kwatra V, Schdl A, Essa I et al (2003) Graphcut textures: image and video synthesis using graph cuts. ACM Transactions on Graphics (ToG) 22(3):277–286
Lee J, et al. (2016) Rich360: optimized spherical representation from structured panoramic camera arrays. ACM Transactions on Graphics (TOG) 35(4):63–73
Lee W-T, Chen H-I, Chen M-S et al (2017) High-resolution 360 video foveated stitching for realtime VR. Computer Graphics Forum 36(7):115–123
Li H, Tang J, Wang Y et al (2012) Looking into the world on Google maps with view direction estimated photos. Neurocomputing 95(14):72–77
Liu Y, Nie L, Han L, et al. (2015) Action2Activity: Recognizing Complex Activities from Sensor Data. International Joint Conference on Artificial Intelligence (IJCAI), pages 1617–1623
Liu Y, Nie L, Liu L et al (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115
Liu Y, Zhang L, Nie L, et al. (2016) Fortune Teller: Predicting Your Career Path. AAAI Conference on Artificial Intelligence, pages 201–207
Liu Y, Zheng Y, Liang Y, et al. (2016) Urban water quality prediction based on multi-task multi-view learning. International Joint Conference on Artificial Intelligence (IJCAI)
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Lu J, Zhu Z (2017) Real-time 4k panoramic video stitching based on gpu acceleration. Computer Science 44(8):18–21
Sun D, Roth S, Black MJ (2010) Secrets of optical flow estimation and their principles. Computer Vision and Pattern Recognition (CVPR), pages 2432–2439
T S, Y N, Z Z, et al. (2016) Video stitching for handheld inputs via combined video stabilization. SIGGRAPH ASIA
W J, J G. (2015) Video stitching with spatial-temporal content preserving warping. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 42–48
Wei X, Mulligan J (2013) Panoramic video stitching from commodity hdtv cameras. Multimedia Systems 19(5):407–426
Y SJ, C LY, J LG et al (2014) Dynamic image stitching for panoramic video. International Journal of Engineering and Technology Innovation 4(4):260–268
Yong J, Wang Y, Lei X, Wang S (2017) Panoramic video stitching method based on improved orb feature detection. Computer Applications and Software 34(5):182–188
Zaragoza J, Chin TJ, Brown MS, et al. (2013) As projective-as-possible image stitching with moving dlt. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2339–2346
Zhang F, Liu F (2014) Parallax-tolerant image stitching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3262–3269
Zhu Z, Lu JM, Wang MX, et al. (2016) A comparative study of algorithms for realtime panoramic video blending. Computer Vision and Pattern Recognition (CVPR)
Funding
This work was supported by the National Natural Science Foundation of China under Grant 61472035 and Grant 61425013.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(MP4 44,000 kb)
Rights and permissions
About this article
Cite this article
Liu, Q., Su, X., Zhang, L. et al. Panoramic video stitching of dual cameras based on spatio-temporal seam optimization. Multimed Tools Appl 79, 3107–3124 (2020). https://doi.org/10.1007/s11042-018-6337-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-6337-2