## 3 System Overview
### ROI Management
#### 3.2.3 ROI Adjustment
### Xroi-Dct

The proposed system sets different compression rates for ROI and non-ROI to increase the compression, thus increasing network transmission efficiency. The high-frequency deletion technique is applied to achieve extra compression on the non-ROI area to achieve such an effect. The high-frequency deletion technique produces additional compression by zeroing the tail values of the discrete cosine-transformed macroblocks. After applying a discrete cosine transform, the valuable information in the macroblock tends to be concentrated in the low-frequency section. By masking the high-frequency section, we can achieve additional compression by sacrificing the minimum amount of image quality. XROI-DCT is only applied to non-ROI regions, where information loss is affordable. By giving up the higher frequencies, higher compression can be achieved. ### UAV Identification

One of the goals of the proposed system is to integrate the UAV identification module into the system. UAV identification is the task of determining the product model of the target UAV. Like other well-known classifiers, such as AlexNet [30], Inception [31], and SSD [32], authors have implemented a UAV identifier--a UAV model classification based on CNN--to recognize the type of threat that the system is tracking. The implemented CNN is trained with UAV image data collected from the web and manually labeled to learn and distinguish UAV models. Our CNN is trained to detect multi-rotors. We chose to train our CNN with multi-rotors because such UAVs are more difficult to distinguish by models--even with human eyes. The images used to train were 1835 images, applied with data augmentation of 16 different UAV models, and 390 images were used for testing. Although it is a relatively small set of images for training a CNN, due to limited classes of UAV models, the neural network was able to adapt well enough to identify UAV models. ## 4 Performance Evaluation

This section provides a brief description of the experiment setting with a thorough evaluation of the system conducted with the prototype of our proposed algorithm. The system is evaluated by encoding UAV videos from Kaggle and YouTube and videos taken by the authors with Samsung Galaxy S10 Ultra. To further evaluate the system performance, the encoded video is compared to the video produced by YOLOv7. The prototype of the system was implemented on a standard desktop machine running Ubuntu 20.04 LTS with Intel Xeon 16 processor CPU and 16 GB RAM. The usage of GPU was restricted to mimic the limited resource environment. YOLOv7 was adopted for object detection, and OpenCV libraries were adopted for Optical Flow calculation and DCT operation. FFMPEG was used for spatial/temporal prediction. Since YOLOv7 could not classify UAVs into models while being able to classify other objects (cars, trucks, desks, etc.) in the environment, the authors have implemented CNN for UAV identification with Tensorflow. The models that the CNN was trained for are listed in the following: 2 DR Solo, DJI Phantom, DJI Inspire, DJI mavic, Chroma, Yuneec Typhoon, Yuneec Typhoon 4K, Xplorer, AscTec FireFly, Ehang 184, DJI Matrice, DJI S1000, XAG V40, Wingsland S6, MJX X916H, and Walkera QR Y100. ### Experimental Configuration

The proposed system is evaluated by encoding a sequence of images retrieved from YouTube, Kaggle, and recorded flying UAVs. The number-labeled videos, a flying Solo2 in an urban setting, were taken by the authors. The videos all have different numbers of frames and different resolutions. Table 1 lists details of the videos encoded for the evaluation. The quality and overall file size of the video are compared to the video produced by YOLOv7. ### Accuracy of ROI Tracking

In this subsection, the accuracy of the optical flow-assisted ROI tracking technique is evaluated. The system can accurately set ROI by performing UAV detection on every frame. However, since UAV detection is a costly algorithm on limited resource devices, the proposed system adopts optical flow to track the displacement of the ROI.