You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper presents an algorithm and the implementation of a motor schema to aid the visual localization subsystem of the ongoing EMILY project at Texas A and M University. The EMILY project aims to team an Unmanned Surface Vehicle (USV) with an Unmanned Aerial Vehicle (UAV) to augment the search and rescue of marine casualties during an emergency response phase. The USV is designed to serve as a flotation device once it reaches the victims. A live video feed from the UAV is provided to the casuality responders giving them a visual estimate of the USVs orientation and position to help with its navigation. One of the challenges involved with casualty response using a USV UAV team is to simultaneously control the USV and track it. In this paper, we present an implemented solution to automate the UAV camera movements to keep the USV in view at all times. The motor schema proposed, uses the USVs coordinates from the visual localization subsystem to control the UAVs camera movements and track the USV with minimal camera movements such that the USV is always in the cameras field of view.

Cited Abstract(s):
[1]: This paper presents the visual localization subsystem of the ongoing EMILY project. This project aims to assist responders with establishing contact with drowning victims as quickly as possible through the use of a 1.3-meter autonomous unmanned surface vehicle (USV). Once victims are reached, the device can serve as a flotation device to support them. An unmanned aerial vehicle (UAV) provides a live video feed to responders and aids USV's navigation by visually estimating the USV's position and orientation. The movement of the UAV and the USV along with varying lighting, distance, and perspective make the task of estimating USV's position and orientation challenging. We present two implemented solutions for reliable visual localization, the first relying on color thresholding and contour detection, and the second using histograms, back-projection, and CamShift algorithm. The visual localization system was validated in four proof-of-concept field trials. Other unsuccessful methods are discussed.
Previous work by shows the evaluation of algorithms for the visual localization subsystem and establishes the histogram based approach to be the most effective in determining the location and orientation of the USV from the UAV's video feed [1] . This paper focuses on the extension of Dufek's work by adding an autonomous system to control the camera's pan tilt to track the USV. In this section, we evaluate multiple techniques to use pan-tilt movements for visual servoing.