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An Autonomous Triaxial UAV Camera Function
An Autonomous Triaxial UAV Camera Function
The development of surveillance technology has become a major driving force in the field of UAV (unmanned aerial vehicle) applications.triaxial uav camera tracking function It has been applied in various fields such as information collection, military reconnaissance, and target tracking. It is also widely used for traffic management, as well as providing emergency services. Surveillance is a critical tool to detect and prevent emerging unusual events, as well as maintain safety and security. However, conducting surveillance manually requires a significant amount of human effort. This is especially true when the target objects are located in confined areas or difficult to access. A UAV is an ideal solution for these tasks, as it can be easily maneuvered in a confined area and can capture remote scene in real-time.
Despite its advantages over traditional methods, the process of surveillance using a drone is complex due to the need for a skilled pilot to stabilize it midair.triaxial uav camera tracking function This article aims to introduce an autonomous object tracking UAV camera function, which eliminates the need for manual control and facilitates creation of quality aerial video contents. The system’s functionality was modeled using Unified Modeling Language tools and deployed on a web platform. It utilizes socket networking in Python programming language to maximize speed while ensuring low latency. An effective object tracking algorithm was employed in the system.
The UAV would initially detect the target object and determine its location.triaxial uav camera tracking function It then determines whether the object is within its range of camera vision and if it is moving too fast to be tracked. If the drone decides that the target object has moved too far away or out of its camera range, the tracking procedure will be automatically terminated.
Once the tracking procedure is terminated, the drone would return to its stable mode and enable the pilot to manually control it via its RC transmitter. Depending on the command given, the drone will either follow a face or an entire body. This functionality can be activated or deactivated by a toggle switch on the UI.
A number of research efforts have been conducted in the past to improve the performance of UAV-based surveillance systems. These efforts mainly focus on increasing the efficiency of tracking algorithms. For example, Chung et al. [1] used a standard but relatively old method that uses background subtraction and frame differencing to track targets. However, the results were not satisfactory.
Other studies have also tried to enhance the tracking capabilities of a UAV by merging position and appearance information. DB-Tracker and SimpleTrack perform better than their peers by effectively utilizing both features. Other trackers such as Deep SORT, ByteTrack, and UAVMOT have further improved their performance by improving the loss function, bounding-box estimation, and detection algorithms.
Several studies have explored the possibility of integrating visual tracking and GPS-based tracking for autonomous UAVs without the need for manual teleoperation. However, the reliability of visual tracking on long-term tracking in unexpected operating environments is questionable. To address this issue, references [2] and [19] propose a hybrid approach that combines the high accuracy of visual tracking with the robustness of GPS-based tracking.
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