Track Anything: Segment Anything Meets Videos

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.

arXiv commentTech-report

Cited by in corpus (6)

• Deep learning for 3D human pose estimation and mesh recovery: A survey
• Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning
• Don't waste SAM
• Emergence of Human-Like Attention in Self-Supervised Vision Transformers: an eye-tracking study
• UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
• Towards Class-agnostic Tracking Using Feature Decorrelation in Point Clouds