Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentation
This addresses the problem of maintaining object tracking over extended periods in video analysis for applications like surveillance or autonomous systems, though it is incremental as it builds on existing interactive and semi-automatic methods.
The paper tackles long-term video object segmentation by introducing Lazy Video Object Segmentation (ziVOS), which uses on-the-fly user interactions based on uncertainty estimation to balance performance and robustness, achieving competitive results on the LVOS dataset with minimal user corrections.
In this paper, we introduce a variant of video object segmentation (VOS) that bridges interactive and semi-automatic approaches, termed Lazy Video Object Segmentation (ziVOS). In contrast, to both tasks, which handle video object segmentation in an off-line manner (i.e., pre-recorded sequences), we propose through ziVOS to target online recorded sequences. Here, we strive to strike a balance between performance and robustness for long-term scenarios by soliciting user feedback's on-the-fly during the segmentation process. Hence, we aim to maximize the tracking duration of an object of interest, while requiring minimal user corrections to maintain tracking over an extended period. We propose a competitive baseline, i.e., Lazy-XMem, as a reference for future works in ziVOS. Our proposed approach uses an uncertainty estimation of the tracking state to determine whether a user interaction is necessary to refine the model's prediction. To quantitatively assess the performance of our method and the user's workload, we introduce complementary metrics alongside those already established in the field. We evaluate our approach using the recently introduced LVOS dataset, which offers numerous long-term videos. Our code is publicly available at https://github.com/Vujas-Eteph/LazyXMem.