# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 1 Introduction  
In recent years, UAVs have been widely used in military reconnaissance, agricultural planting, forest patrol and other fields. Aerial photography images and videos obtained by UAV have high application value due to their wide viewing angle and low occlusion. Identifying, locating, and tracking valuable targets in aerial images or video is an important part of aerial imagery applications.  
The characteristics of aerial image are rich content, varied scene, high angle of view and wide field of view. But the region occupied by the target has a small overall length and is easily occluded by the environment. This puts forward higher requirements for the target detection and tracking process.  
In the aerial images, the size of the target is small. Compared with the whole image, the number of pixels occupied by the region of target is small. In this case, the traditional features of the target, such as the red, green and blue (RGB) feature, will reduce the ability to describe the target. Convolution neural network use convolution kernel to enlarge the sensory field of each pixel. Through the convolution layer, a low-resolution region can merge the surrounding pixel information. Therefore, aiming at the small target in aerial images, we use the convolution feature to obtain better feature expression ability. In order to simplify the model structure and reduce the model parameters, deep features are extracted using their corresponding convolution networks according to different detection modules.  
This paper proposes a real-time detection-tracking framework for tracking a predetermined target in an aerial real-time picture or video sequence. First, target tracking needs to determine the location of the target. We use the target detection algorithm to determine the target in the initial frame. Then, the comparison algorithm is used to determine whether the predetermined target appears in the picture. Finally, the initial position of the predetermined target is sent to the target tracking algorithm and starts the tracking process.  
The framework adopts a combination of target detection and target tracking to ensure tracking accuracy and avoid target loss caused by environmental factors such as occlusion. In this paper, Faster R-CNN [1] and YOLOv3 [2] are used as target detection algorithms respectively, and their convolutional layer is used as the deep features of the target tracking module. The ECO [3] visual tracking algorithm is used as target tracking algorithms. We use OTB50 [4] to test the performance of the target tracking algorithm. Finally, some partial fragments of UAV123 dataset [5] and real scenes are used to test the performance of the target detection-tracking framework.  
The main contributions of our work are summarized below:  
* We use the convolution features extracted by target detection algorithm for the target tracking process. The feature map with high resolution extracted from a shallow convolution layer has abundant spatial information. The feature map with low resolution extracted from a deep convolution layer expresses stronger sematic information. Using convolution features during target tracking improves the effect of target tracking.
* We propose an online target matching strategy based on SIFT [6]. The predetermined target can be selected from the target detection algorithms using this strategy.
* We propose a tracking score as a criterion for tracking failure detection. This criterion can be used to determine whether the tracking process has failed.