Classification of Cinematographic Shots Using Lie Algebra and its Application to Complex Event Recognition

Bhattacharya, Subhabrata; Mehran, Ramin; Sukthankar, Rahul; Shah, Mubarak

doi:10.1109/tmm.2014.2300833

Cited by 27 publications

(4 citation statements)

References 29 publications

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“…Since the early 2000s many studies demonstrate that camera motion can be reliably estimated by processing data from the compressed stream [10], [11]. A camera motion classification is for example used in [12] for distinguishing cinematographic shots into these classes: Aerial, Bird-eye, Crane, Dolly, Establishing, Pan, Tilt, and Zoom. Motion activity maps (MAM) as in [13] keep into account both global motion and its spatial distribution in the scene.…”

Section: A Related Workmentioning

confidence: 99%

Who is the Film's Director? Authorship Recognition Based on Shot Features

et al. 2019

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We show how low-level formal features, such as shot duration, meant as length of camera takes, and shot scale, i.e. the distance between the camera and the subject, are distinctive of a director's style in art movies. So far such features were thought of not having enough varieties to become distinctive of an author. However our investigation on the full filmographies of six different authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total number of 120 movies analysed second by second, confirms that these shot-related features do not appear as random patterns in movies from the same director. For feature extraction we adopt methods based on both conventional and deep learning techniques. Our findings suggest that feature sequential patterns, i.e. how features evolve in time, are at least as important as the related feature distributions. To the best of our knowledge this is the first study dealing with automatic attribution of movie authorship, which opens up interesting lines of cross-disciplinary research on the impact of style on the aesthetic and emotional effects on the viewers.

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Section: A Related Workmentioning

confidence: 99%

Who is the Film's Director? Authorship Recognition Based on Shot Features

et al. 2019

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“…These results demonstrate the importance of shot type in videos, especially for their emotion and aesthetic analysis. 4…”

Section: Qualitative Resultsmentioning

confidence: 99%

A Unified Framework for Shot Type Classification Based on Subject Centric Lens

Rao

Wang

et al. 2020

Preprint

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Shots are key narrative elements of various videos, e.g. movies, TV series, and user-generated videos that are thriving over the Internet. The types of shots greatly influence how the underlying ideas, emotions, and messages are expressed. The technique to analyze shot types is important to the understanding of videos, which has seen increasing demand in real-world applications in this era. Classifying shot type is challenging due to the additional information required beyond the video content, such as the spatial composition of a frame and camera movement. To address these issues, we propose a learning framework Subject Guidance Network (SGNet) for shot type recognition. SGNet separates the subject and background of a shot into two streams, serving as separate guidance maps for scale and movement type classification respectively. To facilitate shot type analysis and model evaluations, we build a large-scale dataset MovieShots, which contains 46K shots from 7K movie trailers with annotations of their scale and movement types. Experiments show that our framework is able to recognize these two attributes of shot accurately, outperforming all the previous methods. 1 1 The dataset and related codes are released here in compliance with regulations.

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“…pan and zoom. Bhattacharya et al [2] presented a discriminative representation of video shot which can effectively distinguish among eight cinematographic shot classes: aerial, bird-eye, crane, dolly, establishing, pan, tilt and zoom. Li et al [19] constructed a videography dictionary to represent the foreground and background motion of each video clip.…”

Section: Related Workmentioning

confidence: 99%

One-Shot Imitation Drone Filming of Human Motion Videos

Huang

Dang

Chen

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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Imitation learning has been applied to mimic the operation of a human cameraman in several autonomous cinematography systems [4,3,5,12]. To imitate different filming styles, existing methods train multiple models, where each model handles a particular style and requires a significant number of training samples. As a result, existing methods can hardly generalize to unseen styles. In this paper, we propose a framework, which can imitate a filming style by "seeing" only a single demonstration video of the same style, i.e., one-shot imitation filming. This is done by two key enabling techniques: 1) feature extraction of the filming style from the demo video, and 2) filming style transfer from the demo video to the new situation. We implement the approach with deep neural network and deploy it to a 6 degrees of freedom (DOF) real drone cinematography system by first predicting the future camera motions, and then converting them to the drone's control commands via an odometer. Our experimental results on extensive datasets and showcases exhibit significant improvements in our approach over conventional baselines and our approach can successfully mimic the footage with a unseen style.

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Classification of Cinematographic Shots Using Lie Algebra and its Application to Complex Event Recognition

Cited by 27 publications

References 29 publications

Who is the Film's Director? Authorship Recognition Based on Shot Features

Who is the Film's Director? Authorship Recognition Based on Shot Features

A Unified Framework for Shot Type Classification Based on Subject Centric Lens

One-Shot Imitation Drone Filming of Human Motion Videos

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