Nguyen, Hoang Quoc Jamsrandorj, Ankhzaya Chao, Vanyi Oo, Yin May Robbani, Muhammad Amrulloh Mun, Kyung Ryoul Kim, Jinwook 2025-12-29T02:00:07Z 2025-12-29T02:00:07Z 2025-11-13 2025-06-12 https://pubs.kist.re.kr/handle/201004/153881 Volleyball video analytics require precisely detecting both the timing and location of key events. We introduce a novel task: Precise Spatiotemporal Event Spotting, which seeks to accurately determine when and where important events occur within a video. To this end, we created the Volleyball Nations League (VNL) Dataset, including 8 full games, 1,028 rally videos, and 6,137 annotated events with both temporal and spatial localization. Our best model, the Spatiotemporal Event Spotter (STES), outperforms the current state-of-the-art (SOTA) in temporal action spotting by 9.86 mean Temporal Average Precision (mTAP) and achieves a notable 80.21 mAP for spatial localization, accurately pinpointing event locations within a 2–6 pixel range. To the best of our knowledge, this is the first work addressing Precise Spatiotemporal Event Spotting in volleyball, establishing a strong baseline for future research in this domain. The code and data for this paper are available publicly at: https://hoangqnguyen.github.io/stes/ English IEEE Computer Society VNL-STES: A Benchmark Dataset and Model for Spatiotemporal Event Spotting in Volleyball Analytics Conference 10.1109/CVPRW67362.2025.00584 1 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.5852 - 5861 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 5852 5861 US Nashville, TN, USA 2025-06-11 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops