EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos

Deng, Dazhen; Wu, Jiang; Wang, Jiachen; Wu, Yihong; Xie, Xiao; Zhou, Zheng; Zhang, Hui; Zhang, Xiaolong; Wu, Yingcai

doi:10.1145/3411764.3445431

Cited by 21 publications

(6 citation statements)

References 49 publications

(56 reference statements)

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“…Labeling is a task that builds the basis for many supervised learning methods and data management in general, including text [12], images [13], audio [14], and video [15], [16]. Since good label quality is crucial for analysis and learning, manual work is often necessary and takes a significant amount of time during data preparation.…”

Section: Visual Labeling and Active Learningmentioning

confidence: 99%

Active Gaze Labeling: Visualization for Trust Building

Koch,

Cao,

Weiskopf

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Areas of interest (AOIs) are well-established means of providing semantic information for visualizing, analyzing, and classifying gaze data. However, the usual manual annotation of AOIs is timeconsuming and further impaired by ambiguities in label assignments. To address these issues, we present an interactive labeling approach that combines visualization, machine learning, and user-centered explainable annotation. Our system provides uncertainty-aware visualization to build trust in classification with an increasing number of annotated examples. It combines specifically designed EyeFlower glyphs, dimensionality reduction, and selection and exploration techniques in an integrated workflow. The approach is versatile and hardware-agnostic, supporting video stimuli from stationary and unconstrained mobile eye tracking alike. We conducted an expert review to assess labeling strategies and trust building.

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Section: Visual Labeling and Active Learningmentioning

confidence: 99%

Active Gaze Labeling: Visualization for Trust Building

Koch,

Cao,

Weiskopf

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…The frame-shot-scene hierarchy in movie analysis [42] and the object-event-tactic hierarchy in sports video annotation [43] can be viewed conjunctively to illustrate the complexity and interdependency of video events. To streamline the exploration, EventAnchor [44] traced visually available objects in racket sports videos and denoted their critical change of states as anchors. These anchors were plotted on the screen to indicate the objects' locations for interactive calibration of the machine errors.…”

Section: Event Understanding In Video Visual Analyticsmentioning

confidence: 99%

“…Apparatus. Presenting multimodal features in a VA system generally leads to better task performance than a baseline system without much computational support [32], [33], [44]. However, it could be unfair to compare Anchorage with both operational and behavioral anchors to a naive baseline because of the wide interaction gap and the compound effect.…”

Section: User Studymentioning

confidence: 99%

Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

Kwai¹,

Wang²,

Wang³

et al. 2023

Preprint

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Delivering customer services through video communications has brought new opportunities to analyze customer satisfaction for quality management. However, due to the lack of reliable self-reported responses, service providers are troubled by the inadequate estimation of customer services and the tedious investigation into multimodal video recordings. We introduce Anchorage, a visual analytics system to evaluate customer satisfaction by summarizing multimodal behavioral features in customer service videos and revealing abnormal operations in the service process. We leverage the semantically meaningful operations to introduce structured event understanding into videos which help service providers quickly navigate to events of their interest. Anchorage supports a comprehensive evaluation of customer satisfaction from the service and operation levels and efficient analysis of customer behavioral dynamics via multifaceted visualization views. We extensively evaluate Anchorage through a case study and a carefully-designed user study. The results demonstrate its effectiveness and usability in assessing customer satisfaction using customer service videos. We found that introducing event contexts in assessing customer satisfaction can enhance its performance without compromising annotation precision. Our approach can be adapted in situations where unlabelled and unstructured videos are collected along with sequential records.

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“…Deng et al. [DWW*21] introduced a multiple‐level video annotation approach for sports videos, such as table tennis content exploration. The above‐mentioned approaches concentrated on video annotation, and did not integrate with a visual analytics pipeline for data exploration.…”

Section: Related Workmentioning

confidence: 99%

“…We note that obtaining the landing position of a shuttle under such a circumstance demands high‐quality shuttle tracking and 3D trajectory reconstruction techniques. While automation is challenging, a possible approach to overcome this problem is to annotate the positions by leveraging human resources [DWW*21]. Second, our automatic data extraction is imperfect.…”

Section: Qualitative Evaluationmentioning

confidence: 99%

Exploration of Player Behaviours from Broadcast Badminton Videos

Chen

Shih

et al. 2023

Computer Graphics Forum

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Understanding an opposing player's behaviours and weaknesses is often the key to winning a badminton game. This study presents a system to extract game data from broadcast badminton videos, and visualize the extracted data to help coaches and players develop effective tactics. Specifically, we apply state‐of‐the‐art machine learning methods to partition a broadcast video into segments, in which each video segment shows a badminton rally. Next, we detect players' feet in each video frame and transform the player positions into the court coordinate system. Finally, we detect hit frames in each rally, in which the shuttle moves towards the opposite directions. By visualizing the extracted data, our system conveys when and where players hit the shuttle in historical games. Since players tend to smash or drop shuttles under a specific location, we provide users with interactive tools to filter data and focus on the distributions conditioned by player positions. This strategy also reduces visual clutter. Besides, our system plots the shuttle hitting distributions side‐by‐side, enabling visual comparison and analysis of player behaviours under different conditions. The results and the use cases demonstrate the feasibility of our system.

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EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos

Cited by 21 publications

References 49 publications

Active Gaze Labeling: Visualization for Trust Building

Active Gaze Labeling: Visualization for Trust Building

Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

Exploration of Player Behaviours from Broadcast Badminton Videos

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