# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 1 Introduction  
In recent years, low-orbiting video satellites have gradually become a frontier technology in the development of remote sensing because of their ability to quickly acquire high-resolution video data over large areas [1]. Compared to traditional optical remote-sensing satellites, the most important feature of video satellites is that they can acquire dynamic information concerning the Earth's surface. Rich information on vehicle dynamics in satellite videos is significant for urban traffic planning, road construction, and smart city buildings. Therefore, the detection and tracking of moving vehicles using satellite video have become a current research hotspot in remote sensing [2]. Compared with normal ground-based videos, satellite videos must consider their unique characteristics when detecting and tracking moving vehicles. The problems of scale variation and inter-target occlusion faced by ground-based videos have been addressed less by satellite videos [3]. However, the high-altitude nature of video satellites allows for a wide range of images, resulting in backgrounds containing many complex features and moving targets that appear faint and tiny. Simultaneously, video satellite gaze-imaging properties cause stationary targets to appear in motion between frames, that is, parallax pseudo-motion [4]. Consequently, traditional motion target detection algorithms cannot effectively identify true motion targets, pseudo-motion targets, and noise. In addition, in the tracking phase, the vehicle identity cannot be effectively identified because of similar vehicle characteristics. Therefore, accurate detection and tracking of moving vehicles from satellite videos remains a difficult task [5]. For this reason, many scholars have proposed the use of prior road knowledge to optimize target detection algorithms [6; 7; 8; 9]. Detection precision has been significantly improved by creating a road mask to reject a large amount of noise and pseudo-motion interference from the background. However, this approach faces various problems in establishing constraints. Methods based on the accumulation of motion information use time domain information. This type of algorithm has poor stability, cannot effectively accumulate complete masks when traffic volumes are low, and requires a significant amount of time prior to detection. Methods based on single-frame object-oriented classification use spatial domain information. This type of method is computationally complex and suffers from problems such as road misses and breakages. Methods based on loading existing road network information utilize existing results. This method requires considerable storage space and the manual creation of road network data in advance. During the tracking phase, the focus is on correlating the detected targets between frames. Previously, data association was performed by constructing motion features [10; 11; 12]. With the rapid development of neural networks, methods based on depth features have gradually become mainstream [13; 14]. However, the small size, large number, and similarity of the features of the moving vehicles in the satellite video make consistently tracking the vehicles between frames using only depth features difficult. Previous research has clearly shown that most algorithms suffer from three problems:  
1 Most algorithms construct a priori constraints considering only time domain or spatial domain information and do not fully combine the two, resulting in poor algorithm stability. 2 Most algorithms are less effective in dealing with faint and small moving targets. 3 During the tracking phase, most algorithms rely excessively on deep features. Therefore, this study proposed a satellite video moving vehicle detection and tracking method based on spatiotemporal characteristic constraints called Detection and Tracking Based on Spatiotemporal Characteristics (DTSTC). The remainder of this paper is organized as follows: Section 2 provides an overview of the existing research relevant to this study.