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Gyroscope camera mount11/25/2022 ![]() The RC units can control the camera's roll, pitch, and yaw while simultaneously viewing the scene the camera is visualizing. The display is integrated with one of two Futaba 2.4GHz remote control (RC) units, depending on the payload platform used. An on-board 1.2GHz video transmitter streams the recorded video to a live display on the ground. High-definition video is recorded directly to the camera's storage device in raw, uncompressed format. The Xsens IMU and GPS are interfaced through a serial communication protocol to the netbook, whose hard disk stores the metadata. A Dell Inspiron mini-9 netbook computer that weighs 2.28lbs serves as the communication link with the IMU and GPS, and is mounted on the payload platform.įigure 3 shows the communications architecture of our system. This unit provides the necessary metadata during collection, including the longitude, latitude, height, roll, pitch, and yaw of the visual sensor. The payload platforms-non-gyroscope-stabilized (left) and gyroscope-stabilized (right).Īn integrated inertial measurement unit (IMU) and global positioning system (GPS) from Xsens Technologies BV are mounted on the pan-tilt-roll unit, with its local axes aligned with those of the camera, but offset by a calculated amount. ![]() The vehicle's movement helps simulate motion in the aerial videos at reasonable velocities depending on the line tension.įigure 2. ![]() The balloon is tethered to a vehicle with an electric winch and can be controlled to reach the required altitude. It can lift a rated payload of 34lbs while assuring an even weight distribution and line tension across the entire envelope. Our aerial platform consists of a 13' Kingfisher Aerostat helium balloon (see Figure 1) that is designed to provide a stable operating platform in winds up to 50mph. ![]() At the University of Florida's Compute Vision Lab, we have developed a platform to overcome these shortcomings. The combined need for metadata and high-resolution imagery also requires sufficient disk-storage space, which adds to the aerial platform's payload and eliminates the possibility of using small-scale, fixed-wing aircraft or rotorcraft for data collection. Additionally, it is particularly useful to obtain information about the sensor's location and orientation (referred to as metadata) which can be valuable in post-processing. To develop algorithms for wide-area surveillance systems and high-resolution imagery acquisition, it is necessary to simulate and get representative datasets from aerial platforms. The performance of existing computer-vision algorithms is typically evaluated on certain standard datasets that have low resolution and a narrow field-of-view. ![]() 2 are attempting to provide high-resolution imagery over a wide field-of-view. For example, the Defense Advanced Research Project Agency's Autonomous Real-time Ground Ubiquitous Surveillance-Imaging System (ARGUS-IS) 1 and Geospatial Systems Inc. Since the performance of any automated detection or tracking algorithm is directly proportional to the GSD, several solutions to persistent wide-area surveillance systems at high resolution are being developed. This determines the ground-sampling distance (GSD), or the number of pixels on an image target. Often, a trade-off between the field-of-view and the imagery resolution is required to focus on a specific activity on the ground. Airborne platforms, including autonomous unmanned vehicles, deployed in sensitive areas play an important role in activity-monitoring and threat-detection, and are increasingly being used for intelligence and reconnaissance missions. Computer-vision algorithms for automatic-target detection and tracking in aerial imagery are being developed to assist in surveillance. ![]()
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