Capturing Captivating Moments: A Multi-Model Approach for Identifying Baseball Strikeout Highlights

doi:10.1007/s11760-024-03805-x

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Title:	Capturing Captivating Moments: A Multi-Model Approach for Identifying Baseball Strikeout Highlights
Authors:	Qiaoyun Zhang, Chih-Yung Chang, Cuijuan Shang;Chang, Hsiang-Chuan;Roy, Diptendu Sinha
Keywords:	Action recognition;Object detection;Heterogeneous features;Multi-model integration
Date:	2025-01-23
Issue Date:	2025-03-20 09:24:21 (UTC+8)
Abstract:	With the extensive popularity of baseball, fans are eager to relive exciting moments such as strikeouts, catching, and home runs. However, manually extracting these highlights from long videos is time-consuming and labor-intensive. To address this, this paper introduces a mechanism called CCM (capturing captivating moments), which aims to effectively identify baseball strikeout highlights. Initially, the proposed CCM employs a coarse-grain policy that involves two key components. Firstly, it utilizes You Only Look Once (YOLO) to detect the change of out-indicator in video frames. Secondly, it employs long short-term memory to analyze the skeleton features of athletes, aiming to extract the draft segments as the candidates that contain the strikeout. Then a fine-grain policy integrates YOLOv5, bidirectional encoder representations from transformers, and 3D convolutional neural networks to accurately identify the strikeout highlights. By combining heterogeneous features and multi-model integration, the proposed CCM ensures robust and precise identification of captivating strikeout moments in baseball videos. The simulation results demonstrate that the proposed CCM outperforms the existing mechanisms in terms of accuracy, recall, precision, and F1-score.
Relation:	Signal, Image and Video Processing 19(232)， p. 1-14
DOI:	10.1007/s11760-024-03805-x
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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