## 2 Related Works
### Preprocessing for the Collection of KMNs  
The aim of preprocessing is to extract spatiotemporal information of activity of the executors corresponding to different KMNs. Therefore, the first task of the proposed framework is to detect and identify these executors from the surveillance videos and thus obtain spatial information in the image plane. Based on this, the temporal motion information of the object is extracted by associating the same object in consecutive video frames. Therefore, detection and recognition are key aspects of the module of preprocessing for the collection of KMNs. Object detection and recognition are hot and fundamental topics in the field of computer vision ([PERSON] et al., 2018). Before the emergence of deep learning-based detectors, handcrafted features, such as the histogram of oriented gradients ([PERSON] et al., 2018) and scale-invariant feature transform ([PERSON] et al., 2018), were used widely. One of the most representative handcrafted feature-based detectors is the DPM, which is based on histogram-of-oriented-gradients feature maps and has been applied in GAMTOS ([PERSON] et al., 2018). With the success of AlexNet ([PERSON] et al., 2018), convolutional neural network-based models have become the mainstream in the field of object detection owing to their powerful capabilities of feature representation. Existing deep learning-based detectors can generally be divided into two categories: two-stage and one-stage detectors. Two-stage methods have two steps, namely, region proposal generation and object classification. As a representative method of two-stage detectors, Faster RCNN was developed using the region proposal network as a proposal generator ([PERSON] et al., 2018). Many two-stage detectors based on the original Faster RCNN have been developed. In general, due to their computational complexity, two-stage detectors suffer from obvious shortcomings when applied to systems with high requirements for real-time performance. To address the real-time problem, region-free one-stage detectors have been proposed. They replace the results of the region proposal network by directly using each cell as a proposal and omit the proposal generation stage. In our opinion, AirNet proposed by [PERSON] et al. belongs to a one-stage detector ([PERSON] et al., 2018). As the most widely used one-stage \begin{table}
\begin{tabular}{c c c c} \hline
**Related Work** & **Detection** & **Tracking** & **KMNs Collection** \\ \hline \multirow{4}{*}{[PERSON] et al. (2018)} & AirNet (In terms of its architecture, AirNet is a one-stage detector with a module of bidirectional feature pyramid network module) & By comparing the positions of object bounding boxes and classes between previous and current frames, the same object in adjacent frames is matched. & Activity identification by the relationship between the involved objects and aircraft, the speed of the object, and the Intersection over Union (IoU) of the object and aircraft. \\  & [PERSON] et al. (2018) & YOLO v3 & The MOSSE (Minimum Output Sum of Squared Error) & Ground services are recognized by using the motion status (stopping and moving) of the vehicles providing services. \\ \multirow{4}{*}{Ours} & Improved YOLO v5 to obtain more precise bounding boxes. & Position prediction and bounding boxes of association in adjacent frames based on a single target and nodes based on the interaction of two targets. \\  & & frames based on IoU. & \\ \hline \end{tabular}
\end{table} 
Table 1: A brief comparison of the proposed work with the latest related works from the perspectives of the specific implementation. detectors at present, the first generation of the YOLO series was proposed in 2015 [28]. The greatest advantage of the original version of the YOLO series (e.g., YOLOv1, YOLOv2, and YOLOv3) is computational speed [29].