## 1 Introduction  
As airports continue to grow in size, accidents occurring on airport surfaces have accounted for 90 percent of civil aviation accidents for the past 20 years [1]. The operational safety of airport surfaces is the core of civil aviation safety. However, due to the imperfection of surveillance facilities, trespassing into restricted airport areas and unlawful interference with airport orders occur from time to time [2]. Therefore, real-time and robust surveillance of individual targets on the airport surface, especially around the fuselage, is essential. Existing airport surface surveillance technologies can be divided into two categories: cooperative and non-cooperative surveillance. The cooperative surveillance systems must install radio transponders on the surveillance objects [3]. This results in the monitoring of fixed targets only and poor flexibility. The non-cooperative surveillance techniques provide only the distance and location of the target [4]. This results in limited information and higher costs. In contrast, video surveillance systems have several advantages, including their low investment cost, wide monitoring range, automation, and intelligent analysis [5]. Current video surveillance devices can usually only capture and save video images, with insufficient capabilities for more advanced functions and intelligent analyses [6]. Only after a hazardous event has occurred can the saved surveillance video be analyzed manually to understand the details and process of the event. This approach is prone to response delays when dealing with sudden hazardous events. This makes it difficult to obtain information for decision-making in real time [7]. Therefore, there is an urgent need to design an airport surface terminal surveillance system that can autonomously analyze the behavior of humans moving on the airport surface based on captured video sequences in real time [8]. As shown in Figure 1, typical non-cooperative objects in airport scenes are small in size in surveillance videos. This makes it difficult to accurately capture the detailed characteristics of this target. At the same time, special vehicles and aircrafts moving across the airport field can also obscure typical non-cooperative objects [9]. The first core task of this research is to build an intelligent surveillance framework that integrates modules for target detection, human keypoint detection, and behavioral classification to address these problems. The non-cooperative object detection module builds on the collaborative processing of multiple telephoto auto-zoom lenses. The overall detection task can be viewed as a two-stage \"coarse to fine\" process. The first stage is regular surveillance. The surveillance camera maintains a wide-angle model to monitor the apron area in all directions. The initial target detection is for all the people within the surveillance area. When a suspicious person is detected entering the apron area, the surveillance camera will be switched to the telephoto model autonomously to reduce the coverage of the surveillance area and to pay close attention to the targets within the local surveillance area. In the second stage, with the multi-scale non-cooperative object localization mode, we can capture the behavioral details of this part of the target and overcome the effects of factors such as lighting and target texture. This module uses deep learning [10] for precise target localization and behavioral recognition to extract the key features of the target. At the same time, the module uses adaptive feature extraction and correction techniques for problems such as lighting and target texture changes to ensure an accurate analysis of the target behavior. Through this multi-stage surveillance and analysis process, the problem of scale inconsistency in airport surface surveillance images caused by the distance and angle of surveillance cameras can be overcome.