Personal Multi-view Viewpoint Recommendation based on Trajectory Distribution of the Viewing Target

Wang, Xueting; Hara, Kensho; Enokibori, Yu; Hirayama, Takatsugu; Mase, Kenji

doi:10.1145/2964284.2967265

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Wang et al proposed an automatic camera switching system for soccer. They calculated scores for each camera based on the presence of a ball or player [14], [15], trajectory of viewing target [17], [18] and interest of viewer groups [16]. Tang and Boring developed a video highlighting system [11].…”

Section: Related Workmentioning

confidence: 99%

Construction of a Switching Support System for Live Broadcast of Oral Presentation

Endo

Takegawa

Funaki

et al. 2021

Journal of Information Processing

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In this paper, we propose a switching support system for live broadcast of oral presentation. Analysis of the switching of professional switchers revealed that, in 57 oral presentations, professional switchers carry out switching based on events such as the presenter moving onto a new slide or pointing to the screen. In this paper, with the goal of even switching beginners being able to do switching intuitively, we proposed an event-based switching interface reflecting special characteristics of switching. We implemented a prototype of the proposed system and used it to conduct an evaluation experiment. The results of the experiment suggested that the proposed method can be operated as easily as the previous method and that the proposed method conforms to professional switching more than the previous method.

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Section: Related Workmentioning

confidence: 99%

Construction of a Switching Support System for Live Broadcast of Oral Presentation

Endo

Takegawa

Funaki

et al. 2021

Journal of Information Processing

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“…Camera Viewpoint Prediction In single camera systems, previous techniques have been proposed to predict camera angles for PTZ cameras [7] and to generate natural-looking normal field-of-view (NFOV) video from 360 o panoramic views [33,32,22]. In multi-camera systems, camera viewpoint prediction methods select a subset of all available cameras [39,12,35,3,17,18,43,40,26]. In broadcast systems, semi-automatic [16] and fully automatic systems have been developed in practice.…”

Section: Related Workmentioning

confidence: 99%

Learning Sports Camera Selection From Internet Videos

Chen

Tian

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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This work addresses camera selection, the task of predicting which camera should be "on air" from multiple candidate cameras for soccer broadcast. The task is challenging because of the scarcity of learning data with all candidate views. Meanwhile, broadcast videos are freely available on the Internet (e.g. Youtube). However, these videos only record the selected camera views, omitting the other candidate views. To overcome this problem, we first introduce a random survival forest (RSF) method to impute the incomplete data effectively. Then, we propose a spatialappearance heatmap to describe foreground objects (e.g. players and balls) in an image. To evaluate the performance of our system, we collect the largest-ever dataset for soccer broadcasting camera selection. It has one main game which has all candidate views and twelve auxiliary games which only have the broadcast view. Our method significantly outperforms state-of-the-art methods on this challenging dataset. Further analysis suggests that the improvement in performance is indeed from the extra information from auxiliary games.

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“…Our previous study [22] proposed a personal viewpoint recommendation method by modeling the relationship between a viewer's personal viewpoint-selection tendency and the ball trajectory distribution of a soccer game. In this paper, we include trajectory distributions of more objects as the features to model personal viewpoint-selection tendency to adapt to possible interests of different viewers.…”

Section: User Preference Based Viewpoint Navigationmentioning

confidence: 99%

“…Besides, most of the related studies focus mainly on common preferences [13], [16]- [18] and professional editing rules [19], [20]. Only several studies considered personal preference [21], [22]. In this study, we focus on the viewer's personal preference on spatiotemporal object dynamics for viewpoint selection.…”

mentioning

confidence: 99%

Personal Viewpoint Navigation Based on Object Trajectory Distribution for Multi-View Videos

Wang

Hara

Enokibori

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYMulti-camera videos with abundant information and high flexibility are useful in a wide range of applications, such as surveillance systems, web lectures, news broadcasting, concerts and sports viewing. Viewers can enjoy an enhanced viewing experience by choosing their own viewpoint through viewing interfaces. However, some viewers may feel annoyed by the need for continual manual viewpoint selection, especially when the number of selectable viewpoints is relatively large. In order to solve this issue, we propose an automatic viewpoint navigation method designed especially for sports. This method focuses on a viewer's personal preference for viewpoint selection, instead of common and professional editing rules. We assume that different trajectory distributions of viewing objects cause a difference in the viewpoint selection according to personal preference. We learn the relationship between the viewer's personal viewpoint-selection tendency and the spatio-temporal game context represented by the objects trajectories. We compare three methods based on Gaussian mixture model, SVM with a general histogram and SVM with a bag-of-words to seek the best learning scheme for this relationship. The performance of the proposed methods are evaluated by assessing the degree of similarity between the selected viewpoints and the viewers' edited records. key words: multi-view video navigation, user preference, Gaussian mixture model

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Personal Multi-view Viewpoint Recommendation based on Trajectory Distribution of the Viewing Target

Cited by 7 publications

References 17 publications

Construction of a Switching Support System for Live Broadcast of Oral Presentation

Construction of a Switching Support System for Live Broadcast of Oral Presentation

Learning Sports Camera Selection From Internet Videos

Personal Viewpoint Navigation Based on Object Trajectory Distribution for Multi-View Videos

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