## 1 Introduction
For many practical applications, objects with large aspect ratios, dense distributions, and various orientations need to be detected; for example, in scene text detection or remote sensing object detection, objects can be arbitrarily oriented. The above-mentioned horizontal detectors have fundamental limitations for these applications. Thus, many rotation detectors [12; 13] based on a general detection framework have been proposed. In remote sensing object detection, many rotation detectors have been proposed [14; 15; 16; 17] and have achieved promising performances on large-scale public datasets for aerial images, e.g., DOTA [18], HRSC2016 [19], and OHD-SJTU [20]. Among these remote sensing targets, the detection of aircraft achieved almost the best accuracy. For object tracking, most modern computer vision approaches are based on tracking-by-detection [21], where a set of possible objects are first identified via object detectors. Based on the detection results from each frame, a Kalman filter, particle filter, and probabilistic model are often used to accomplish accurate and consecutive object tracking. Meanwhile, deep learning methods have also attracted considerable interest in the visual tracking community as robust visual trackers. According to their architectures, state-of  
Figure 1: Comparison of remote sensing methods between ground processing and on-orbit processing. the-art deep learning-based visual tracking methods are categorized as convolutional neural networks (CNNs) [22; 23], Siamese neural networks (SNNs) [24; 25], recurrent neural networks (RNNs) [26], or generative adversarial networks (GANs) [27]. Although these state-of-the-art methods have achieved significant progress, they are still not reliable for real-world applications. To achieve on-board detection and tracking of aircraft in a remote sensing video, the usual tracking-by-detection methods are not feasible. This is because each detection procedure is computationally intensive, and the computing and memory resources of micro-satellites are limited. However, the excellent results achieved by rotation detectors in aircraft detection can serve as a good initial input for the tracking procedure. Thus, in this paper, an aircraft target tracking algorithm based on rotation detectors and image matching is proposed. First, aircraft detection was performed on the first incoming remote sensing image using a robust DCNN-based object detection model. Then, a multi-target tracking model based on image matching was proposed for efficient aircraft tracking. Finally, incorporating geospatial information, the aircraft activity was recognized in terms of its motion speed. The main contributions of this paper are as follows:  
1. Since the high-value activities of aircraft at an airport usually occur on the runway, we focus on detecting aircraft in the area around the runway. The smaller the detection area, the faster the detection procedure. Aircraft detection was implemented using a rotation detector named R3 det. 2. The tracking algorithm can effectively cope with various challenging situations, such as the negative influence of various backgrounds and lighting conditions, self-rotation of aircraft, and aircraft entering and exiting. 3. Combining the results of aircraft tracking and geospatial information, aircraft activities were divided into parking, taxiing, and flying in terms of the aircraft's motion speed. The satellite can selectively save and download the video data of interest according to the activity recognition results, reducing the amount of satellite-to-ground data transmission. 4. The algorithm was verified to be efficient and effective using remote sensing videos from commercial micro-satellites. The remainder of this paper is organized as follows. In Section 2, we outline our algorithm. In Section 3, the deep convolutional neural network (DCNN) based aircraft detection is introduced. Aircraft tracking and activity recognition is introduced in Section 4.