Video object segmentation (VOS) aims to distinguish and track target objects in a video. Despite the excellent performance achieved by off-the-shell VOS models, existing VOS benchmarks mainly focus on short-term videos lasting about 5 seconds, where objects remain visible most of the time. However, these benchmarks poorly represent practical applications, and the absence of long-term datasets restricts further investigation of VOS in realistic scenarios. Thus, we propose a novel benchmark named LVOS, comprising 720 videos with 296,401 frames and 407,945 high-quality annotations. Videos in LVOS last 1.14 minutes on average, approximately 5 times longer than videos in existing datasets. Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objects. Compared to previous benchmarks, our LVOS better reflects VOS models' performance in real scenarios. Based on LVOS, we evaluate 20 existing VOS models under 4 different settings and conduct a comprehensive analysis. On LVOS, these models suffer a large performance drop, highlighting the challenge of achieving precise tracking and segmentation in real-world scenarios. Attribute-based analysis indicates that key factor to accuracy decline is the increased video length, emphasizing LVOS's crucial role. We hope our LVOS can advance development of VOS in real scenes. Data and code are available at https://lingyihongfd.github.io/lvos.github.io/.

翻译：视频对象分割（VOS）旨在区分并追踪视频中的目标对象。尽管现有VOS模型已取得优异性能，但现有基准数据集主要聚焦于约5秒的短时视频，且其中目标对象在大部分时间内保持可见。然而，这类基准难以反映实际应用场景，长时数据集的缺失限制了VOS在真实场景中的进一步研究。为此，我们提出名为LVOS的新型基准数据集，包含720个视频、296,401帧及407,945个高质量标注。LVOS中视频平均时长为1.14分钟，约为现有数据集的5倍。每个视频包含多种属性，尤其是源自野外环境的挑战性特征，如长期重现与跨时相似对象。与先前基准相比，LVOS能更准确反映VOS模型在真实场景中的性能。基于LVOS，我们在4种不同设置下评估了20个现有VOS模型并开展综合分析。这些模型在LVOS上性能显著下降，凸显了真实场景中实现精准追踪与分割的挑战。基于属性的分析表明，精度下降的关键因素在于视频时长增加，这强调了LVOS的核心价值。我们期望LVOS能推动VOS在真实场景中的发展。数据与代码已开源在https://lingyihongfd.github.io/lvos.github.io/。