# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 4 Evaluation
### Performance of Aerial Video  
The performance of the target detection tracking framework was tested using partial segments of the UAV123 dataset. UAV123 is a new dataset with sequences from an aerial viewpoint. It has a subset of which is meant for long-term aerial tracking. The car1 sequence in the UAV123 dataset was tested. When calculating the tracking score, the first frame of the tracking process is selected as the standard score \(P_{S}\), The subsequent tracking score \(P_{C}\) is calculated, and the relative score \(P_{C}/P_{S}\) is calculated. When the relative score is less than 0.3, it is an indication that the object is occluded or tracking has failed, and then target detection is performed again. While framework is initializing, five images are randomly selected from the image in which the target appears in the entire sequence as the initial feature of the SIFT matching algorithm. Initializing the SIFT math algorithm with initial features is shown in Figure 6.  
The results of the framework evaluation using the YOLOv3 detection module and the Tdarknet tracking module is described as follows. The target detection module successfully detects the target in the first frame. The tracking module performs target tracking after the matching. The speed of the frame during the entire detection and tracking process is 11 FPS. The Car1 sequence is a process in which the target vehicle is driving away. In the tracking process of 1800 frames, three target losses occurred. PSR during the tracking process is shown in Figure 7. Tracking losses occurred at the ABC three points in the figure. The car in A has a large rotation compared with the car in the first frame. Some features in the initial tracking frame are occluded. Therefore, the PSR is less than the threshold. In the next frame after loss, the target detection module successfully found the target back. The car in B has a fast motion during A to B. The PSR is less than the threshold, and the target is successfully retrieved in the next frame after the loss. The car in C has a serious occlusion. After the car is partially blocked, the PSR is less than the threshold. After the car reappears in the field of view, the framework re-detects and retrieves the target again. The result of the framework test using the Faster R-CNN detection module and the TVGG tracking module is described as follows. The target detection module successfully detects the target in the first frame. The tracking module performs target tracking after the matching. The speed of the frame during the entire inspection and tracking process is 12 FPS. Relative PSR during the tracking process is shown in Figure 8. The tracking loss occurs at point A. The car in A has a large rotation compared with the car in the first frame. Some features in the initial tracking frame are occluded, so the PSR is less than the threshold and is lost. However, due to the low accuracy of the Faster R-CNN for small targets, the target detection module does not detect this car in the fragment from the 936 th to the 1800 th frame. Figure 6: Initializing the SIFT math algorithm with initial features. Figure 8: PSR during tracking process with tracker TVGG. Figure 7: Peak to sidelobe ratio (PSR) during tracking process with tracker Tdarknet. This figure shows that the most challenging section of a video can lead to a decrease in PSR. It can be seen from the evaluation that target tracking depends on whether the target detection is successful or not. The target in the aerial view scene is far from the camera, and the view angle is large, so that the target takes a small proportion in the image and the target pixel is blurred. From the comparison of the two detection-tracking frameworks in the above evaluations, we can obtain the following points:  
* Faster R-CNN does not detect small targets well, but YOLOv3 is better for small targets in aerial scenes because of multi-scale detection. * Target tracking is greatly affected by target morphological changes and environmental occlusion.