Segment Any Motion in Videos (2503.22268v2)

Abstract: Moving object segmentation is a crucial task for achieving a high-level understanding of visual scenes and has numerous downstream applications. Humans can effortlessly segment moving objects in videos. Previous work has largely relied on optical flow to provide motion cues; however, this approach often results in imperfect predictions due to challenges such as partial motion, complex deformations, motion blur and background distractions. We propose a novel approach for moving object segmentation that combines long-range trajectory motion cues with DINO-based semantic features and leverages SAM2 for pixel-level mask densification through an iterative prompting strategy. Our model employs Spatio-Temporal Trajectory Attention and Motion-Semantic Decoupled Embedding to prioritize motion while integrating semantic support. Extensive testing on diverse datasets demonstrates state-of-the-art performance, excelling in challenging scenarios and fine-grained segmentation of multiple objects. Our code is available at https://motion-seg.github.io/.

Overview of "Segment Any Motion in Videos"

The paper "Segment Any Motion in Videos" introduces a novel methodology for the precise segmentation of moving objects within video data, a task that is pivotal for high-level visual understanding essential in areas such as autonomous navigation and interactive 3D reconstruction. The authors address the limitations commonly found in traditional approaches that predominantly rely on optical flow for motion cues, which often struggle with issues like partial motion, motion blur, and complex background dynamics.

Methodology

The proposed framework departs from traditional techniques by integrating long-range trajectory motion cues with a DINO-based semantic feature extraction and enhances mask density through SAM2's iterative prompting strategy. The novel components introduced in this paper include:

1. Spatio-Temporal Trajectory Attention (STTA): This mechanism captures both spatial relationships among trajectories and temporal dynamics within individual trajectories, allowing robust detection of motion patterns over varying time spans.
2. Motion-Semantic Decoupled Embedding (MSDE): Here, the integration of motion cues with semantic data is strategically decoupled, ensuring that the segmentation process prioritizes motion information while leveraging semantic support to enhance accuracy.

The model is trained across a broad spectrum of datasets, encompassing synthetic and real-world examples, facilitating strong generalization capabilities even with minimal real-world training data.

Performance and Analysis

The authors conducted extensive evaluations on multiple benchmarks, including DAVIS16, DAVIS17, FBMS-59, and SegTrack V2, showcasing state-of-the-art performance. Key achievements of their approach are particularly notable in environments with dynamic backgrounds and when handling finely detailed segmentation tasks of multiple moving objects. The paper reports superior metrics in region similarity (J) and contour accuracy (F), thereby validating the robustness and precision of their methodology.

Implications and Future Work

The approach outlined in this paper holds considerable promise for advancing video object segmentation tasks in practical scenarios like autonomous driving and video analytics where the distinction between camera and object movement is crucial. The ability to segment fine-grained details will likely improve machine perception in navigation systems and enhance interactive media technologies.

The authors' work hints at future developments in this field which could entail a deeper exploration into more nuanced integration of semantic and motion cues, or an expansion into more comprehensive datasets that could refine the existing models' capacities to handle even more complex and diverse environments. Moreover, leveraging advancements in hardware capabilities to process such sophisticated models in real-time would significantly enhance the applicability of such frameworks in dynamic real-world scenarios.

By overcoming the traditional limitations associated with optical flow and by proposing a unified framework that adeptly synthesizes motion and semantic information, this paper lays a foundation for the next generation of video analysis techniques focused on motion segmentation.