Track Anything: Segment Anything Meets Videos

Autor:	Yang, Jinyu, Gao, Mingqi, Li, Zhe, Gao, Shang, Wang, Fangjing, Zheng, Feng
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Tech-report
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.11968 Zobrazit plný text záznamu View this record from Arxiv