# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Detection through Video/Images  
Vision-based UAV detection techniques mainly focus on image processing. Cameras and videos are used to capture the images of UAVs. Then, using artificial vision techniques, UAVs positions are estimated. A vision-based UAV detection approach is presented in [37]. This approach consists of an online recognition system for the identification of 3D objects. The system uses a black-and-white television camera to provide a 2D image on a digital computer. After \begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Range & 500 m \\ Coverage azimuth angle (min–max) & 10\({}^{\circ}\)–90\({}^{\circ}\) \\ Audio spectrum & 0–96 kHz \\ Microphone range & 50–80 m \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Dedrone DroneTracker audio detector specifications. obtaining the image on the computer, the next step is to remove the clutter from the image by means of a preprocess that provides a clean silhouette as well as its boundaries. At the time of the calculations, certain characteristics are obtained and are used to identify the objects, the position they occupy and their orientation in space by means of a recognition algorithm. A similar system is the one developed in [38], which makes use of classical vision algorithms. This system starts by taking the first image, which is used for initialization of the background estimation. Then a loop is started where the trajectories are predicted in the capture time for each new image taken by the cameras. All those pixels that are different from the background that was previously estimated are detected and form one or more blobs related to the current targets. These blobs are extracted using trajectory predictions, edge detectors and motion detectors. With blobs and an association process, one on more blobs are associated to each target, and in addition, the blobs within the association are used to initialize the tracks. Finally, each track is updated with its corresponding blobs, and the not-updated tracks are deleted. In contrast, nonconventional segmentation methods make use of neural networks to directly identify the appearance of UAVs. For example, in [39], the authors developed a system that is capable of detecting, recognizing, and tracking a UAV using a single camera automatically. For that purpose, a single Pan-Tilt-Zoom (PTZ) camera detects flying objects and obtains their tracks; once a track is identified as a UAV, it locks the PTZ control system to capture the detailed image of the target region. Afterward, the images can be classified into the UAV and interference classes (such as birds) by a convolution neural network classifier trained with an image dataset. The identification accuracy of track and image reaches 99.50% and 99.89%, respectively. This system could be applied in a complex environment where many birds and UAVs appear simultaneously. It is possible to detect UAVs from the cameras of other UAVs. An approach for online detection of small UAVs and estimation of their positions and velocities in a 3D environment from a single moving (on-board) camera is presented in [40]. The methods used are computationally light, despite the complexity of computer vision algorithms, so they may be used on UAVs with limited payload. This approach incorporates fast object detection using an AdaBoost-based tracking algorithm. Real-time performance with accurate object detection and tracking is possible, enabling the tracker to extract the position and size of an aircraft from a video frame. The detections are given to a multitarget tracker to estimate the aircraft's position and velocity in 3D. The effectiveness of this method has been proven with an indoor experiment with three quadrotors. In [41], a general architecture for a highly accurate and computationally efficient UAV-to-UAV detection and tracking algorithm from a camera mounted on a moving UAV platform was developed. The system is composed of a moving target detector followed by a target tracker.