  Object tracking is a fundamental task in computer vision with broad practical
applications across various domains, including traffic monitoring, robotics,
and autonomous vehicle tracking. In this project, we aim to develop a
sophisticated aerial vehicle system known as Track Anything Rapter (TAR),
designed to detect, segment, and track objects of interest based on
user-provided multimodal queries, such as text, images, and clicks. TAR
utilizes cutting-edge pre-trained models like DINO, CLIP, and SAM to estimate
the relative pose of the queried object. The tracking problem is approached as
a Visual Servoing task, enabling the UAV to consistently focus on the object
through advanced motion planning and control algorithms. We showcase how the
integration of these foundational models with a custom high-level control
algorithm results in a highly stable and precise tracking system deployed on a
custom-built PX4 Autopilot-enabled Voxl2 M500 drone. To validate the tracking
algorithm's performance, we compare it against Vicon-based ground truth.
Additionally, we evaluate the reliability of the foundational models in aiding
tracking in scenarios involving occlusions. Finally, we test and validate the
model's ability to work seamlessly with multiple modalities, such as click,
bounding box, and image templates.
