Summary of Track Anything Rapter(tar), by Tharun V. Puthanveettil et al.
Track Anything Rapter(TAR)
by Tharun V. Puthanveettil, Fnu Obaid ur Rahman
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The project aims to develop a sophisticated aerial vehicle system called Track Anything Rapter (TAR) that can detect, segment, and track objects of interest based on user-provided multimodal queries. TAR utilizes pre-trained models like DINO, CLIP, and SAM to estimate the relative pose of the queried object, approaching the tracking problem as a Visual Servoing task. The system integrates these foundational models with advanced motion planning and control algorithms deployed on a custom-built PX4 Autopilot-enabled Voxl2 M500 drone. To validate the tracking algorithm’s performance, it is compared against Vicon-based ground truth. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The TAR system allows users to track objects of interest using multimodal queries like text, images, and clicks. It uses advanced computer vision models to detect and track objects, and then uses motion planning and control algorithms to keep the drone focused on the object. The system is tested against real-world scenarios and compared to a ground truth. |
Keywords
» Artificial intelligence » Sam » Tracking
