# TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically
## 2 Related Works  
Many countries are interested in aircraft and parachute tracking. After years of continuous exploration, researchers have proposed and implemented various tracking methods. With improvements in computer performance, computer vision is attracting more attention and computer visual tracking technology has become one of the most popular research topics. Despite the impressive performance of convolutional neural networks (CNNs) [11], traditional tracking algorithms have irreplaceable positions in scenes with high real-time requirements and no training sets. Due to the high flying speed of an aircraft and the high speed of a parachute from its ejection to its opening, the pilot chute will soon fly out of the view of the camera if the tracking target is not switched from plane to chute in time. The camera is fixed on the turntable. As the turntable rotates, the camera also rotates, which means that the target is tracked in the dynamic background. In order to enable the turntable to follow the aircraft and parachute targets, a tracking algorithm is required to detect the foreground target rapidly under the dynamic background. Hence, a tracking algorithm with a good real-time performance is needed to satisfy the requirement. Background subtraction [12] and frame difference [13] are commonly used to detect a moving target. These methods segment static background information and move foreground objects from the frame sequence. Background subtraction involves obtaining the moving target; the background image is subtracted from the current frame in the video sequence. It shoots a fixed scene with a static camera [14]. However, the actual background could change, e.g., changes in illumination and shaking of the camera, which will interfere in the detection of moving targets [15]. In recent years, scholars have conducted a lot of research on the background models; many methods were introduced to solve the above-mentioned problems. Evaluations and comparisons for 29 background difference methods can be found in the research [12], e.g., weighted moving mean [16], adaptive background learning [17], Gaussian mixture model (GMM) [18], etc. However, these methods require a lot of calculation times or the detection effects are not very good. The frame difference involves calculating the difference of two adjacent frames in a video sequence with the moving target in the foreground [13]. However, two obvious disadvantages exist in these methods based on the frame difference. On the one hand, the speed of the frame and the moving target are quite big. On the other hand, the detected foreground targets have aperture and ghosting [19]. However, the aperture and ghosting have little influence on tracking the switched target. For the aperture, what we need is the outline of the foreground but not the full foreground [20]. For the ghosting, the camera is mounted on a turntable; the turntable keeps the target in the center of the field of view without causing a greater displacement of the target in the field of view, which thereby effectively curbs the ghosting phenomenon [21]. Regarding a relatively simple background, frame differences can obtain good detection results, have good performances in computing power, and are not sensitive to light intensity changes [22]. The paper is mainly based on the detection of aircraft and parachutes with quite simple backgrounds, such as the sky. As the camera is mounted on the turntable and rotates with the turntable, we can obtain a sky background from different angles; the light intensity of the background also changes rapidly with the rotation. In order to enable the turntable to track the target in real time, the motion detection algorithm needs to have a better real-time performance. The frame difference has good performance in terms of light sensitivity and real-time requirements. Therefore, the frame difference method is used as the target detection algorithm for the motion foreground. Optical flow is one of the most widely used tracking algorithms for computer vision [23].