SegAnyMo: Automatically Identify and Precisely Segment Moving Objects in Videos 140

What is SegAnyMo?

SegAnyMo (Segment Any Motion in Videos) is an intelligent "video matting" tool. It can automatically identify and precisely segment moving objects in videos. Without manual annotation, it can segment multiple moving objects in videos and handle scenarios with fast-moving objects, blurry frames, and complex backgrounds.

Research Background

Moving object segmentation is a key task for achieving high-level understanding of visual scenes, with numerous downstream applications. Previous research has mostly relied on optical flow to provide motion cues, but this approach often leads to inaccurate predictions when faced with challenges such as partial motion, complex deformation, motion blur, and background interference.

Method Introduction

This research proposes a new moving object segmentation method 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. The specific process is as follows:

1. Input Processing: Uses 2D trajectories and depth maps generated by off-the-shelf models as input, captures motion patterns through a motion encoder, and generates feature trajectories.

2. Trajectory Decoding: Uses a trajectory decoder that integrates DINO features to decode the feature trajectories. By decoupling motion and semantic information, it ultimately obtains dynamic trajectories.

3. Mask Generation: Uses SAM2 to group dynamic trajectories belonging to the same object and generates fine-grained moving object masks.

SegAnyMo Features

• Moving Object Detection and Segmentation: Automatically detects moving objects from videos and generates corresponding segmentation masks, accurately identifying the contours and positions of the moving objects.

• Handling Complex Scenes: Capable of handling complex scenarios such as partial motion, complex deformation, motion blur, and background interference, while still accurately segmenting target objects.

• Multi-Object Segmentation: Can simultaneously detect and segment multiple moving objects in videos, generating independent segmentation masks for each object for convenient individual processing and analysis.

• Generalization Capability: Has strong generalization capability, adapting to different video datasets and scenarios, and maintaining high segmentation accuracy even in unseen scenarios.

• Incorporating Semantic Information: By combining semantic information, it more accurately segments moving objects and avoids misidentifying static objects as moving ones.

• Real-Time Performance and Efficiency: Meets real-time processing requirements to a certain extent and can be applied in scenarios with high real-time demands.

• Support for Multiple Inputs: Supports various input data such as 2D trajectories and depth maps, and can flexibly adjust segmentation strategies according to different input conditions.

• Scalability: The architecture design has good scalability, allowing performance improvements or adaptation to new task requirements by adding or adjusting modules.

Technical Advantages

• Spatiotemporal Trajectory Attention Mechanism: Through this mechanism, the model can better focus on motion information while incorporating semantic support, enabling accurate segmentation of moving objects even in complex scenes.

• Motion-Semantic Decoupled Embedding: Avoids erroneous judgments caused by the model's over-reliance on semantic information, such as misclassifying a static road surface as a dynamic object.

• Strong Generalization Capability: Extensive testing on multiple datasets shows that the model performs excellently in challenging scenes and fine-grained segmentation of multiple objects.

Application Scenarios

• Video Surveillance: Real-time detection of moving targets such as pedestrians and vehicles, reducing false alarms and improving the accuracy of security monitoring.

• Autonomous Driving: Identifies dynamic obstacles on the road, assisting autonomous driving decision-making.

• Sports Video Analysis: Segments athlete movements, supporting analysis of complex actions and multi-person scenarios.

• Intelligent Security: Avoids false alarms from static objects, improving system reliability.

• Video Editing: Classifies and processes different moving targets, facilitating the addition of special effects and target replacement.

• Traffic Monitoring: Analyzes multi-vehicle movement, supporting traffic flow management.

• Robot Vision: Uses depth maps to segment moving targets, assisting robot navigation and obstacle avoidance.

• Multi-Person Video Conferencing: Segments speaker images, enhancing the interactive experience.

• Academia and Development: Open-source code facilitates reproduction and secondary development, supporting training and testing on new datasets.

Project Homepage:https://motion-seg.github.io/

Github Address:https://github.com/nnanhuang/SegAnyMo