A Method to Detect and Track Moving Airplanes from a Satellite Video

Abstract

In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F1 score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated.

Detecting and Tracking Moving Airplanes from Space Based on Normalized Frame Difference Labeling and Improved Similarity Measures

“…Zhang et al [10] also employed ViBe to detect moving cars with an emphasis on eliminating parallax motion. Shi et al [1] presented an Improved Gaussian-based Background Subtractor (IPGBBS) to detect moving airplanes in a satellite video. Compared with ViBe, IPGBBS has a better performance on false alarm suppression, but it may not provide satisfactory detection for low-contrast objects (termed camouflage problem).…”

“…Their performance under complex conditions, such as rotation and leave-the-scene, remains unknown. To effectively track rotating objects, Shi et al [1] proposed a rotation-invariant multi-object tracking algorithm named primary scale invariant feature transform keypoint matching (P-SIFT KM). The advantages of P-SIFT keypoint are their high distinctiveness, repeatability, and rotation invariance.…”

“…Recursive moving object detection algorithms such as ViBe [19,24] and IPGBBS [1] usually require sufficient input frames to adapt the background model before it can achieve stable detection, which is undesired for a satellite video of short duration (up to 90 s). In contrast, our NFDL algorithm adopts a non-recursive strategy for moving airplane detection that does not require background model adaption.…”

“…In the last decade, new satellite sensors capable of capturing videos have been developed and launched [1][2][3][4][5][6][7][8][9][10][11][12]. Unlike satellite images, the temporal resolution of a satellite video is determined by its frame rate.…”

“…Using satellite video data to detect moving objects may encounter similar challenges as ordinary videos [18], such as foreground aperture, camouflage, and parallax motion. Although existing algorithms can address such problems with various adaptive models [1,3,10], stable detection may not be achieved for the early stage of the video [19]. Ideally, moving object detection and tracking algorithms should be integrated into a unified method so that the outcomes of detection can automatically provide the initial locations of moving objects immediately for tracking.…”