Track Anything: Segment Anything Meets Videos (2304.11968v2)

Abstract: Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its impressive segmentation performance on images. Regarding its strong ability on image segmentation and high interactivity with different prompts, we found that it performs poorly on consistent segmentation in videos. Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos. To be detailed, given a video sequence, only with very little human participation, i.e., several clicks, people can track anything they are interested in, and get satisfactory results in one-pass inference. Without additional training, such an interactive design performs impressively on video object tracking and segmentation. All resources are available on {https://github.com/gaomingqi/Track-Anything}. We hope this work can facilitate related research.

• The paper presents the Track Anything Model (TAM) that combines SAM and XMem to minimize manual annotation in video segmentation.
• It employs a four-step process including prompt-driven initialization, semi-supervised tracking, and refinement to achieve JF scores of 88.4 (DAVIS-2016) and 73.1 (DAVIS-2017).
• The approach significantly enhances video processing efficiency and opens avenues for improved long-term memory and mask correction in complex scenes.

Track Anything: High-performance Interactive Tracking and Segmentation

This paper introduces the Track Anything Model (TAM), a novel approach for interactive tracking and segmentation in video sequences. The model leverages the capabilities of the Segment Anything Model (SAM) and the XMem video object segmentation (VOS) framework to address challenges in temporal correspondence and minimize the need for manual annotations.

Background and Motivation

Video Object Tracking (VOT) and Video Object Segmentation (VOS) are critical tasks in computer vision, often requiring substantial human input for dataset annotation and initialization. Traditional methods rely on large-scale, manually annotated datasets and predefined object masks, which can be labor-intensive and time-consuming. The Segment Anything Model (SAM) was developed to mitigate these issues in static images through robust segmentation abilities and interactive prompts. However, its application directly to video proved suboptimal due to inadequate temporal coherence.

Proposed Methodology

The authors integrate SAM and XMem in a unified framework designed for efficient video segmentation. TAM operates in a four-step process:

1. Initialization with SAM: Uses prompt-driven segmentation to generate initial masks, requiring minimal user clicks.
2. Tracking with XMem: Deploys semi-supervised VOS to track the object across subsequent frames, optimally utilizing both spatial and temporal features.
3. Refinement with SAM: Addresses potential inaccuracies in XMem’s predictions through SAM-based mask refinement using interactive prompts.
4. Human Correction: Allows user intervention to correct or improve mask quality, ensuring adaptability to complex scenarios.

Experimental Evaluation

The method was benchmarked on the DAVIS-2016 and DAVIS-2017 datasets, achieving $J_{\text{F}}$ scores of 88.4 and 73.1, respectively, indicating competitive performance against existing state-of-the-art methods. These results demonstrate TAM’s capability in handling intricate scenes, object deformations, and camera motion.

Implications and Future Directions

TAM offers a flexible and efficient solution for video annotation and editing tasks, facilitating advancements in interactive video processing applications. The click-based initialization and correction mechanism significantly reduces the time and effort typically required in video annotation tasks, making it a valuable tool for both academic research and practical deployment.

The authors identify potential areas for future research, including improving SAM’s refinement capabilities in complex object structures and enhancing long-term memory handling within VOS models. These advancements could further bolster TAM's application range, particularly in longer, unedited video sequences.

Conclusion

The Track Anything Model represents a noteworthy contribution to the domain of video segmentation, providing a user-friendly interface and robust tracking performance through minimal user interaction. Its integration of state-of-the-art models SAM and XMem underlines the potential for innovative adaptations of existing frameworks to tackle longstanding challenges in computer vision tasks.