###### Abstract  
The automatic collection of key milestone nodes in the process of aircraft turnaround plays an important role in the development needs of airport collaborative decision-making. This article exploits a computer vision-based framework to automatically recognize activities of the flight in/off-block and docking/undocking and record corresponding key milestone nodes. The proposed framework, which seamlessly integrates state-of-the-art algorithms and techniques in the field of computer vision, comprises two modules for the preprocessing and collection of key milestones. The preprocessing module extracts the spatiotemporal information of the executor of key milestone nodes from the complex background of the airport ground. In the second module, aiming at two categories of key milestone nodes, namely, single-target-based nodes represented by in-block and off-block and interaction-of-two-targets-based nodes represented by docking and undocking stairs, two methods for the collection of key milestone are designed, respectively. Two datasets are constructed for the training, testing, and evaluation of the proposed framework. Results of field experiments demonstrate how the proposed framework can contribute to the automatic collection of these key milestone nodes by replacing the manual recording method routinely used today. a +
Footnote †: journal: Applied Sciences  
article  
[color=gray!20,][color=gray! aircraft turnaround. The proposed framework automatically identifies the flight in/off-block and docking/undocking activities and records the corresponding KMNs, instead of employing the manual recording method used daily. Existing systems of airport ground surveillance enable the monitoring of moving and parked targets mainly through target-positioning systems [6; 7], such as Automatic Dependent Surveillance-Broadcast (ADS-B), Surface Movement Radar (SMR), and Multilateration (MLAT). The aforementioned systems, which mainly identify and label airport ground targets as point signals, are unable to effectively capture and extract semantic features and detailed information about moving and parked targets, and it is therefore difficult to use these systems to identify flight ground handling processes and collect corresponding KMNs. In recent years, computer vision-based surveillance systems have gained substantial popularity [8; 9]. Two computer vision-based systems for airport ground surveillance, namely, INTERVUSE [10] and AVITRACK [11], were developed in the European Union at the turn of the century. With the development of digital tower conception over the last 20 years, current computer vision-based systems for airport ground surveillance have been exploited [12]. In brief, computer vision-based systems have three advantages in airport ground surveillance. First, computer vision-based systems have lower equipment investment costs than A-DSB, SMR, and MLAT. Second, unlike ADS-B and MLAT for cooperative target surveillance and SMR for noncooperative target surveillance, computer vision-based systems can monitor both cooperative and noncooperative ground targets. Third, computer vision-based surveillance uses vision sensors to capture images with rich semantic and detailed information, which is required for the identification of ground handling and corresponding KMNs. From a methodological perspective of KMN collection by using computer vision, we roughly divide the KMNs listed in Figure 1 into two categories. The first category is the KMNs based on a single target, such as In-block, Off-block, Cabin doors open/close, and Cargo doors open/close. To address the problems of collecting KMNs of this category, the core task is to determine the moments when the position or state of the target changes through computer vision techniques. Another category is KMNs based on the interaction of two objectives, such as Docking/Undocking stairs, Catering start/completion, and Refueling start/completion.