Computer Vision and Image Understanding最新文献_第8页

Large Kernel Information-interaction Network for single image super-resolution 单图像超分辨率大核信息交互网络

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.cviu.2025.104633

Yi Wu, Weiwei Wang, Yujie Wang, Kaige Cui

{"title":"Large Kernel Information-interaction Network for single image super-resolution","authors":"Yi Wu, Weiwei Wang, Yujie Wang, Kaige Cui","doi":"10.1016/j.cviu.2025.104633","DOIUrl":"10.1016/j.cviu.2025.104633","url":null,"abstract":"<div><div>Single-image super-resolution (SISR) has achieved substantial progress, enabling high-fidelity restoration from low-resolution inputs. However, the high computational cost of existing methods remains a major challenge, limiting their deployment on resource-constrained edge devices. To address this issue, we propose a lightweight Large Kernel Information Interaction Network (LKIN), which effectively balances computational efficiency and reconstruction quality. Our approach integrates multi-scale large receptive fields, information distillation, and attention mechanisms to enhance feature representation and improve super-resolution performance. Specifically, we replace the conventional BSConv with a large kernel network, allowing the model to capture long-range dependencies more effectively while reducing the reliance on deeper architectures. Additionally, we introduce a Multi-Scale Feature Enhancement (MSFE) module, which leverages efficient convolutions and attention mechanisms to refine extracted features while eliminating redundant operations. Extensive experiments are conducted on standard benchmarks (Set5, Set14, BSD100, Urban100, Manga109) at <span><math><mo>×</mo></math></span> 2, <span><math><mo>×</mo></math></span> 3, and <span><math><mo>×</mo></math></span> 4 upscaling factors. We evaluate performance using PSNR and SSIM. Compared with representative lightweight CNN-based methods (e.g., IMDN, BSRN, CARN) and Transformer-based approaches (e.g., SwinIR-light, SRFormer, ESRT, NGSwin), LKIN achieves up to +0.15 dB PSNR improvements over the strongest baseline while reducing parameters by 18%.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104633"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial Sensitive Grad-CAM++: Towards High-Quality Visual Explanations for Object Detectors via Weighted Combination of Gradient Maps 空间敏感的Grad-CAM++：通过梯度图的加权组合实现高质量的目标检测器视觉解释

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-16 DOI: 10.1016/j.cviu.2026.104658

Toshinori Yamauchi

{"title":"Spatial Sensitive Grad-CAM++: Towards High-Quality Visual Explanations for Object Detectors via Weighted Combination of Gradient Maps","authors":"Toshinori Yamauchi","doi":"10.1016/j.cviu.2026.104658","DOIUrl":"10.1016/j.cviu.2026.104658","url":null,"abstract":"<div><div>Visual explanation for object detectors is crucial for ensuring model reliability and promoting their application across a variety of domains. However, existing visual explanation methods often produce low-quality heat maps that highlight only parts of important regions for detected instances. This limitation reduces the interpretability of object detectors and hinders effective model analysis. To address this issue, we propose Spatial Sensitive Grad-CAM++, a visual explanation method for object detectors designed to enhance the quality of heat maps. The proposed method introduces a weighted combination of gradient maps into the heat map computation, taking into account the architecture of object detectors. This approach enables a more accurate representation of each CNN channel’s contribution to the final heat map, resulting in high-quality heat maps that better identify important regions. In quantitative evaluations using the deletion and insertion metrics, we confirm that the proposed method outperforms existing methods by approximately 30% and 8%, respectively. In qualitative evaluations, we further demonstrate the superiority of the proposed method. These results suggest that the proposed method provides more faithful explanations, allowing for more accurate and reliable model analysis.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104658"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causality-inspired multi-grained cross-modal sign language retrieval 因果关系启发的多粒度跨模态手语检索

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-02 DOI: 10.1016/j.cviu.2025.104631

Xu-Hua Yang, Dong Wei, Wangjie Li, Hongxiang Hu

{"title":"Causality-inspired multi-grained cross-modal sign language retrieval","authors":"Xu-Hua Yang, Dong Wei, Wangjie Li, Hongxiang Hu","doi":"10.1016/j.cviu.2025.104631","DOIUrl":"10.1016/j.cviu.2025.104631","url":null,"abstract":"<div><div>Sign language retrieval aims to enhance communication between the deaf and hearing individuals. Due to the scarcity of sign language video data, researchers often use contrastive learning-based data augmentation methods to mitigate data sparsity. However, the pairwise metric learning paradigm fails to properly account for differences among various augmentations and may even erroneously learn the distinctions between augmentation methods. Moreover, existing sign language retrieval studies are susceptible to spurious correlations between cross-modal data and often overlook associations across different granularities. To address these limitations, we propose a Causality-Inspired Multi-Grained Cross-Modal Sign Language Retrieval method (CMCM) that enhances cross-modal retrieval capabilities by eliminating both observable and unobservable confounders. First, CMCM performs varying degrees of augmentation on the original videos and employs backdoor adjustment to mitigate confounders among augmented data, obtaining highly stable video representations invariant to confounding factors. Next, we propose a cross-modal causal-attention Gaussian network that employs front-door causal intervention to eliminate implicit confounders and parameterize their Gaussian distribution for fine-grained alignment. Finally, we design a temporal-motion covariance pooling method to capture global features of sign language sequences, facilitating coarse-grained cross-modal feature alignment. Extensive experiments on three public datasets demonstrate that CMCM achieves highly competitive retrieval accuracy. The code is available at: <span><span>https://github.com/vddong-zjut/CMCM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104631"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal video segmentation with natural language using text–video cross attention and Bayesian order-priors 基于文本-视频交叉注意和贝叶斯先验的自然语言时域视频分割

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2025-12-31 DOI: 10.1016/j.cviu.2025.104622

Carlos Plou , Lorenzo Mur-Labadia , Jose J. Guerrero, Ruben Martinez-Cantin, Ana C. Murillo

{"title":"Temporal video segmentation with natural language using text–video cross attention and Bayesian order-priors","authors":"Carlos Plou , Lorenzo Mur-Labadia , Jose J. Guerrero, Ruben Martinez-Cantin, Ana C. Murillo","doi":"10.1016/j.cviu.2025.104622","DOIUrl":"10.1016/j.cviu.2025.104622","url":null,"abstract":"<div><div>Video is a crucial perception component in both robotics and wearable devices, two key technologies to enable innovative assistive applications, such as navigation and procedure execution assistance tools. Video understanding tasks are essential to enable these systems to interpret and execute complex instructions in real-world environments. One such task is step grounding, which involves identifying the temporal boundaries of activities based on natural language descriptions in long, untrimmed videos. This paper introduces Bayesian-VSLNet, a probabilistic formulation of step grounding that predicts a likelihood distribution over segments and refines it through Bayesian inference with temporal-order priors. These priors disambiguate cyclic and repeated actions that frequently appear in procedural tasks, enabling precise step localization in long videos. Our evaluations demonstrate superior performance over existing methods, achieving state-of-the-art results in the Ego4D Goal-Step dataset, winning the <em>Goal Step</em> challenge at the EgoVis 2024 CVPR. Furthermore, experiments on additional benchmarks confirm the generality of our approach beyond Ego4D. In addition, we present qualitative results in a real-world robotics scenario, illustrating the potential of this task to improve human–robot interaction in practical applications. Code is released at <span><span>https://github.com/cplou99/BayesianVSLNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104622"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TOTNet: Occlusion-aware temporal tracking for robust ball detection in sports videos TOTNet：运动视频中用于鲁棒球检测的闭塞感知时间跟踪

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-12 DOI: 10.1016/j.cviu.2026.104657

Hao Xu , Arbind Agrahari Baniya , Sam Wells , Mohamed Reda Bouadjenek , Richard Dazeley , Sunil Aryal

{"title":"TOTNet: Occlusion-aware temporal tracking for robust ball detection in sports videos","authors":"Hao Xu , Arbind Agrahari Baniya , Sam Wells , Mohamed Reda Bouadjenek , Richard Dazeley , Sunil Aryal","doi":"10.1016/j.cviu.2026.104657","DOIUrl":"10.1016/j.cviu.2026.104657","url":null,"abstract":"<div><div>Ball tracking is a fundamental problem in computer vision, particularly in sports analytics, where it underpins tasks such as analyzing ball movement in soccer and basketball or detecting bounce locations in tennis and table tennis. Most existing methods are developed and evaluated on resource-rich, commercial sports footage with ideal camera angles, high-resolution imagery, and multiple viewpoints. In contrast, many other sports contexts, including semi-professional leagues, local amateur competitions, and Paralympic sports, lack these resources. Footage in these settings often comes from single, fixed, and suboptimal viewpoints, where occlusion becomes a dominant challenge for automated tracking. Existing methods frequently fall short in such conditions because their architectures and training strategies do not explicitly account for prolonged or full occlusion. To address this gap, we present the <strong>Table Tennis Australia (TTA) dataset</strong>, the first professionally annotated Paralympic table tennis benchmark with dense visibility labels, captured under realistic single-view conditions. With <strong>2,396</strong> occluded instances (including 998 fully occluded), TTA is the most occlusion-rich publicly available dataset to date. Alongside the dataset, we propose the <strong>Temporal Occlusion Tracking Network (TOTNet)</strong>, a novel tracking system designed to maintain localization accuracy even under extended occlusion. Through comprehensive experiments on four sports tracking datasets, TOTNet achieves state-of-the-art performance, with substantial gains in full-occlusion scenarios. We release the dataset, code, and evaluation scripts to foster reproducibility and future research in occlusion robust tracking for low resource sports; all materials are available at <span><span>https://github.com/AugustRushG/TOTNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104657"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal transformer–diffusion framework for large-scale reconstruction of soccer tracking data 足球跟踪数据大规模重建的多模态变压器-扩散框架

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2025-12-29 DOI: 10.1016/j.cviu.2025.104626

Harry Hughes , Patrick Lucey , Michael Horton , Harshala Gammulle , Clinton Fookes , Sridha Sridharan

{"title":"Multimodal transformer–diffusion framework for large-scale reconstruction of soccer tracking data","authors":"Harry Hughes , Patrick Lucey , Michael Horton , Harshala Gammulle , Clinton Fookes , Sridha Sridharan","doi":"10.1016/j.cviu.2025.104626","DOIUrl":"10.1016/j.cviu.2025.104626","url":null,"abstract":"<div><div>In soccer, tracking data (player and ball locations over time) is central to performance analysis and a major focus of computer vision in sport. Tracking from broadcast or single-view video offers scalable coverage across all professional matches but suffers from frequent occlusions and missing information. Existing academic work typically evaluates short clips under simplified conditions, whereas industrial applications require complete, game-level coverage. We address these challenges with a multimodal transformer–diffusion framework that combines human-in-the-loop event supervision with single-view video. Our approach first leverages long-term multimodal context — tracking and event annotations — to improve coarse agent localization, then reconstructs full trajectories using a diffusion-based generative model that produces realistic, temporally coherent motion. Compared to state-of-the-art methods, our approach substantially improves both coarse and fine-grained accuracy while scaling effectively to industrial settings. By integrating human supervision with multimodal generative modeling, we provide a robust and practical solution for producing accurate and realistic player and ball trajectories under challenging real-world single-view conditions.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104626"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CSNet: A content and structure-aware approach for color constancy 色彩稳定性的内容和结构感知方法

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.cviu.2026.104638

Zhuo-Ming Du , Hong-An Li , Qian Yu , Wen-He Chen , Fei-long Han

{"title":"CSNet: A content and structure-aware approach for color constancy","authors":"Zhuo-Ming Du , Hong-An Li , Qian Yu , Wen-He Chen , Fei-long Han","doi":"10.1016/j.cviu.2026.104638","DOIUrl":"10.1016/j.cviu.2026.104638","url":null,"abstract":"<div><div>Accurate estimation and correction of global illuminant color, known as color constancy, is crucial for computational photography and computer vision but remains challenging under complex lighting conditions. We propose CSNet, an end-to-end framework that improves color constancy through a novel content-guided feature fusion approach. The input image is first decomposed into three precomputed components: mean intensity, variation magnitude, and variation direction. These components are dynamically reweighted by the Content-Weighting Network (CWN), which generates spatially varying weight maps by leveraging both local and global image features. The reweighted components are fused via the Adaptive Fusion Module (AFM) to produce an HDR-like intermediate representation. This representation is then processed by the Illumination Prediction Network (IPN), which applies semantic-aware weighting to estimate the global illuminant color as an RGB triplet. Extensive experiments on standard benchmarks demonstrate that CSNet achieves state-of-the-art performance, offering robust and visually consistent results under diverse lighting conditions. These advantages make CSNet a powerful tool for applications such as automatic photo correction and augmented reality.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104638"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive review on advances in instance-level 6D object pose tracking 实例级6D物体姿态跟踪研究进展综述

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-21 DOI: 10.1016/j.cviu.2026.104667

Yanming Wu , Hui Zhang , Patrick Vandewalle , Peter Slaets , Eric Demeester

{"title":"A comprehensive review on advances in instance-level 6D object pose tracking","authors":"Yanming Wu , Hui Zhang , Patrick Vandewalle , Peter Slaets , Eric Demeester","doi":"10.1016/j.cviu.2026.104667","DOIUrl":"10.1016/j.cviu.2026.104667","url":null,"abstract":"<div><div>Instance-level 6D object pose tracking involves tracking a known object in 3D space and estimating its six degrees of freedom (6DoF) pose across consecutive images, starting from the initial pose in the first frame. This technology has wide-ranging applications in various fields, including robotics, augmented reality, and human–machine interaction. Over the years, significant progress has been made in this field. Many methods tackle the problem of instance-level 6D pose tracking from RGB images. These techniques can be classified based on their use of keypoints, edges, region information or direct optimization. Additionally, the availability of affordable RGB-D sensors has prompted the utilization of depth data for 6D pose tracking. Another notable advancement is the adoption of deep neural networks, which have shown promising results. Despite these developments, survey studies on the latest advancements in this field are lacking. Therefore, this work aims to fill this gap by providing a comprehensive review of recent progress in instance-level 6D object tracking, covering the aforementioned advancements. This paper provides a detailed examination of metrics, datasets, and methodology employed in this field. Based on the problem modeling approach, methods reviewed in this paper are categorized into optimization-based, learning-based, filtering-based approaches and hybrid approaches that combine various techniques. Furthermore, quantitative results on several publicly available datasets are presented and analyzed, along with applications and open challenges for future research directions.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104667"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STAR Block: Adaptive spatio-temporal recalibration for action quality assessment STAR块：行动质量评估的自适应时空再校准

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2026-01-12 DOI: 10.1016/j.cviu.2026.104656

Junhao Sun, Lanfei Zhao

{"title":"STAR Block: Adaptive spatio-temporal recalibration for action quality assessment","authors":"Junhao Sun, Lanfei Zhao","doi":"10.1016/j.cviu.2026.104656","DOIUrl":"10.1016/j.cviu.2026.104656","url":null,"abstract":"<div><div>Action Quality Assessment (AQA) aims to quantitatively evaluate the execution quality of complex human actions, which poses significant challenges due to the need for jointly modeling spatio-temporal dynamics and semantic structures. Existing approaches typically rely on static single-branch architectures, limiting their capacity to balance local fine-grained details and global rhythmic dependencies, especially in high-complexity scenarios. To address these limitations, we propose a novel Spatio-Temporal Adaptive Recalibration (STAR) Block, which enables highly discriminative representation learning via a multi-dimensional modeling strategy. Specifically, we first design a Multi-Scale Context Encoder to capture subtle local cues by leveraging parallel convolutions across spatial, temporal, and joint domains, enhancing the perception of motion details and short-term dynamics. Second, we introduce an Axial Attention-Based Global Dependency Modeling Module, which efficiently captures long-range temporal relationships while preserving the original spatio-temporal structure, thus reinforcing the understanding of phase coherence and motion rhythm. Third, a Dynamic Attention-Guided Adaptive Feature Fusion mechanism is proposed to integrate multi-path temporal semantics by assigning adaptive weights to local and global representations, enabling dynamic equilibrium in temporal modeling. Across multiple metrics, our STAR Block delivers remarkably superior performance with significant margins over state-of-the-art methods, achieving an average Spearman’s <span><math><mi>ρ</mi></math></span> improvement of 1.56% on AQA-7, 0.57% on MTL-AQA with DD supervision, and near-perfect 99.52% accuracy on FR-FS, as proven by extensive evaluations.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104656"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boundary-aware semantic segmentation for ice hockey rink registration 基于边界感知的冰球场注册语义分割

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-02-01 Epub Date: 2025-12-27 DOI: 10.1016/j.cviu.2025.104627

Zhibo Wang , Amir Nazemi , Stephie Liu , Sirisha Rambhatla , Yuhao Chen , David Clausi

{"title":"Boundary-aware semantic segmentation for ice hockey rink registration","authors":"Zhibo Wang , Amir Nazemi , Stephie Liu , Sirisha Rambhatla , Yuhao Chen , David Clausi","doi":"10.1016/j.cviu.2025.104627","DOIUrl":"10.1016/j.cviu.2025.104627","url":null,"abstract":"<div><div>Accurate registration of ice hockey rinks from broadcast video frames is fundamental to sports analytics, as it aligns the rink template and broadcast frame into a unified coordinate system for consistent player analysis. Existing approaches, including keypoint- and segmentation-based methods, often yield suboptimal homography estimation due to insufficient attention to rink boundaries. To address this, we propose a segmentation-based framework that explicitly introduces the rink boundary as a new segmentation class. To further improve accuracy, we introduce three components that enhance boundary awareness: (i) a boundary-aware loss to strengthen boundary representation, (ii) a dynamic class-weighted mechanism in homography estimation to emphasize informative regions, and (iii) a self-distillation strategy to enrich feature diversity. Experiments on the NHL and SHL datasets demonstrate that our method significantly outperforms both baselines, achieving improvements of <span><math><mrow><mo>+</mo><mn>2</mn><mo>.</mo><mn>84</mn></mrow></math></span> and <span><math><mrow><mo>+</mo><mn>3</mn><mo>.</mo><mn>48</mn></mrow></math></span> in IoU<sub>part</sub> and IoU<sub>whole</sub> on the NHL dataset, and <span><math><mrow><mo>+</mo><mn>1</mn><mo>.</mo><mn>53</mn></mrow></math></span> and <span><math><mrow><mo>+</mo><mn>5</mn><mo>.</mo><mn>85</mn></mrow></math></span> on the SHL dataset, respectively. Ablation studies further confirm the contribution of each component, establishing a robust solution for rink registration and a strong foundation for downstream sports vision tasks.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104627"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0