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
this https URL

视频对象分割（VOS）旨在在视频中区分和跟踪目标对象。我们提出了一个名为 LVOS 的新基准，比现有的数据集包含长 5 倍的视频，以更好地反映 VOS 模型在实际场景中的性能。在 LVOS 中，我们评估了 20 个现有的 VOS 模型，并发现这些模型在真实场景中遇到了较大的性能下降，突出了在实际世界场景中实现精确跟踪和分割的挑战。