Automatic Camera Selection in the Context of Basketball Game

Florent, Lefevre; Bombardier, Vincent; Charpentier, Patrick; Krommenacker, Nicolas; Petat, Bertrand

doi:10.1007/978-3-319-94211-7_9

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Camera selection is an important part of broadcast directing (i.e., video editing), where the goal is to choose optimal camera views for conveying a story based on various criteria, such as the visibility of objects-of-interest [9,10,21,42,45], smoothness [6,7], "gravity" (i.e., layout) of players [20], and so on. Most such video editing systems are built on the predefined rules-of-thumb [9,10,19,42,45], which are conveyed through computational measurements like ball size, number of subjects, and speaker visible.…”

Section: Related Workmentioning

confidence: 99%

Smart Director: An Event-Driven Directing System for Live Broadcasting

PanYingwei

ChenYue

BaoQian

et al. 2021

ACM Trans. Multimedia Comput. Commun. Appl.

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Live video broadcasting normally requires a multitude of skills and expertise with domain knowledge to enable multi-camera productions. As the number of cameras keeps increasing, directing a live sports broadcast has now become more complicated and challenging than ever before. The broadcast directors need to be much more concentrated, responsive, and knowledgeable, during the production. To relieve the directors from their intensive efforts, we develop an innovative automated sports broadcast directing system, called Smart Director, which aims at mimicking the typical human-in-the-loop broadcasting process to automatically create near-professional broadcasting programs in real-time by using a set of advanced multi-view video analysis algorithms. Inspired by the so-called “three-event” construction of sports broadcast [ 14 ], we build our system with an event-driven pipeline consisting of three consecutive novel components: (1) the Multi-View Event Localization to detect events by modeling multi-view correlations, (2) the Multi-View Highlight Detection to rank camera views by the visual importance for view selection, and (3) the Auto-Broadcasting Scheduler to control the production of broadcasting videos. To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events. It is also the first system to solve the novel problem of multi-view joint event detection by cross-view relation modeling. We conduct both objective and subjective evaluations on a real-world multi-camera soccer dataset, which demonstrate the quality of our auto-generated videos is comparable to that of the human-directed videos. Thanks to its faster response, our system is able to capture more fast-passing and short-duration events which are usually missed by human directors.

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Section: Related Workmentioning

confidence: 99%

Smart Director: An Event-Driven Directing System for Live Broadcasting

PanYingwei

ChenYue

BaoQian

et al. 2021

ACM Trans. Multimedia Comput. Commun. Appl.

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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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“…Moreover, real-time detection of players can be valuable in identifying the opponent's formation and strategy, and might give some insight on the likelihood of a certain play be successful. This can lead to better strategies [Lefevre, Bombardier and Charpentier et al (2018)]. One of the principle challenges in sports science is a target examination of player execution.…”

Section: Introductionmentioning

confidence: 99%

An Automated Player Detection and Tracking in Basketball Game

Kaarthick¹

2019

Computers, Materials &Amp; Continua

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Vision-based player recognition is critical in sports applications. Accuracy, efficiency, and Low memory utilization is alluring for ongoing errands, for example, astute communicates and occasion classification. We developed an algorithm that tracks the movements of different players from a video of a basketball game. With their position tracked, we then proceed to map the position of these players onto an image of a basketball court. The purpose of tracking player is to provide the maximum amount of information to basketball coaches and organizations, so that they can better design mechanisms of defence and attack. Overall, our model has a high degree of identification and tracking of the players in the court. We directed investigations on soccer, basketball, ice hockey and pedestrian datasets. The trial comes about an exhibit that our technique can precisely recognize players under testing conditions. Contrasted and CNNs that are adjusted from general question identification systems, for example, Faster-RCNN, our approach accomplishes cutting edge exactness on three sorts of recreations (basketball, soccer and ice hockey) with 1000×fewer parameters. The all-inclusive statement of our technique is additionally shown on a standard passer-by recognition dataset in which our strategy accomplishes aggressive execution contrasted and cutting-edge methods.

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Automatic Camera Selection in the Context of Basketball Game

Cited by 6 publications

References 10 publications

Smart Director: An Event-Driven Directing System for Live Broadcasting

Smart Director: An Event-Driven Directing System for Live Broadcasting

Learning Sports Camera Selection From Internet Videos

An Automated Player Detection and Tracking in Basketball Game

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