Abstract
In film art, the usage of various fields of view in a movie reflects the shooting style of the director or artist. Analyzing which field of view was used by the shot can help us achieve the semi-automatic design and generation of the storyboard. Before achieving the generation of movie shots, the corresponding field of view recognition method and field of view dataset need to help the machine select a suitable field of view. However, although the traditional field of view recognition consumes many resources and time, no one establishes a relevant field of view dataset successfully. To solve this problem, we propose a method for automatically recognizing the field of view in a movie shot. In addition, we create a new field of view dataset by extracting shot images from available movies, including 10041 pictures. Experiments show that the proposed method can accurately achieve the automatic field of view recognition work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beleznai, C., Bischof, H.: Fast human detection in crowded scenes by contour integration and local shape estimation. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 2246–2253 (2009)
Bo, Y.: Application and thinking of man-machine cooperation technology in film and art creation in the era of virtual production. J. Beijing Film Acad. (11), 7 (2021)
Bordwell, D., Thompson, K.: Film art: an introduction. Film Art An Introduction 24(18), 260–265 (2011)
Dahyot, R., Rea, N., Kokaram, A.C.: Sport video shot segmentation and classification. In: Visual Communications and Image Processing 2003 (2003)
Duan, L.Y., Min, X., Tian, Q.: Semantic shot classification in sports video. In: Storage and Retrieval for Media Databases 2003 (2003)
Ekin, A., Tekalp, A.M.: Shot type classification by dominant color for sports video segmentation and summarization. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings (ICASSP 2003) (2003)
Ekin, A., Tekalp, A.M.: Automatic soccer video analysis and summarization (2004)
Fan, J., Elmagarmid, A.K., Zhu, X., Aref, W.G., Wu, L.: Classview: hierarchical video shot classification, indexing, and accessing. IEEE Trans. Multimedia 6(1), 70–86 (2004)
Ferrer, M.Z., Barbieri, M., Weda, H.: Automatic classification of field of view in video. In: IEEE International Conference on Multimedia & Expo (2006)
Jalal, A., Nadeem, A., Bobasu, S.: Human body parts estimation and detection for physical sports movements. In: International Conference on Communication (2019)
Ke, L., Qi, H., Chang, M.C., Lyu, S.: Multi-scale supervised network for human pose estimation. IEEE (2018)
Kovács, Bálint, A.: Shot scale distribution: An authorial fingerprint or a cognitive pattern? Projections 8(2), 50–70(21) (2014)
Lienhart, R.W.: Comparison of automatic shot boundary detection algorithms. Proc. SPIE 3656(1), 290–301 (1999)
Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE Trans. Pattern Anal. & Mach. Intell. 32(4), 604–18 (2010)
Mascelli, J.V.: The five C’s of cinematography: motion picture filming techniques. The five C’s of cinematography: motion picture filming techniques (1998)
Uehara, K., Amano, M., Ariki, Y., Kumano, M.: Video shooting navigation system by real-time useful shot discrimination based on video grammar. In: IEEE International Conference on Multimedia & Expo (2004)
Yang, L., Song, Q., Wang, Z., Hu, M., Liu, C.: Hier R-CNN: instance-level human parts detection and a new benchmark. IEEE Trans. Image Process. 30, 39–54 (2021)
Acknoledgements
We thank the ACs and reviewers. This work is partially supported by the National Natural Science Foundation of China (62072014 & 62106118), the Beijing Natural Science Foundation (L192040), the Open Fund Project of the State Key Laboratory of Complex System Management and Control (2022111), the Project of Philosophy and Social Science Research, Ministry of Education of China (20YJC760115).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jin, X. et al. (2022). FOV Recognizer: Telling the Field of View of Movie Shots. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-18913-5_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18912-8
Online ISBN: 978-3-031-18913-5
eBook Packages: Computer ScienceComputer Science (R0)