# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 1 Introduction  
Computer vision technology has undergone rapid development and has been widely applied in various domains of life [1, 2], especially in the field of transportation [3]. This technology enables the identification of vehicles in single-camera scenes, allowing for the extraction of vehicle information such as speed, location, trajectory, and license plate [4, 5]. Furthermore, in the context of multi-camera surveillance, it facilitates the association of specific target vehicles, enabling tasks such as vehicle trajectory reconstruction and continuous tracking [6]. Meanwhile, with the advancement of video technology, relying on video to achieve anomalous trajectory identification has the capability to be realised. Vehicle Track Recognition is a critical technology in the field of traffic management and safety, including the identification of dangerous driving behaviour, abnormal speeds, abnormal parking and dangerous lane changing behaviour, etc. [7]. In real-life traffic environments, anomalies in vehicle trajectories affect the behaviour of the rest of the vehicles, often implying a higher probability of traffic accidents, such as vehicle rollovers,collisions and even casualties. Consequently, achieving accurate and fast identification of abnormal vehicle trajectories can improve travel safety and efficiency. Currently, most of the existing techniques in this field focus on performance in terms of accuracy, while efficiency considerations are slightly lacking. For example, the DeepSORT [8] algorithm uses a two-stage target detector, R-CNN [9], which is unable to meet the real-time requirements in real-world scenarios. Meanwhile, it is difficult for most of the existing methods to monitor the vehicle's trajectory throughout the entire journey in ultra-long-distance road scenarios, which makes the automatic identification of trajectory anomalies challenging. This paper proposes an efficient anomalous trajectory recognition architecture for ultra-long distance highways. We design a network architecture based on DeepSORT with YOLOv5s for efficient multi-vehicle target tracking to quickly acquire trajectories in the visible area. However, there is a certain range of blind spots in the field of view of neighbouring cameras in ultra-long-distance road scenarios, which means that we cannot directly access the vehicle trajectory for the whole journey. To overcome the problem, we apply the Bidirectional LSTM structure to predict the trajectory in the blind area. In addition to this, an attribute recognition module is proposed to efficiently implement vehicle re-identification, which means that we can correctly stitch together the trajectories of the same vehicle on different road sections, and eventually recover the full trajectory of the vehicle. This study selects the Hong Kong-Zhuhai-Macao Bridge Road as the research scenario, aiming to investigate specific situations within highway surveillance videos. In this context, there are short-distance blind spots between various monitoring cameras. To address this limitation, the study employs a driving trajectory integration approach. This involves comprehensive analysis and processing of vehicle trajectory data recorded by monitoring cameras, thereby predicting and outlining the motion trajectories of vehicles within the blind spots. Through this method, the study achieves the stitching and reconstruction of vehicle motion trajectories across the entire area, facilitating the effective identification of abnormal trajectories. Figure 1 shows the experimental scene diagram. The contributions of our paper can be summarized as follows:  
1. We proposes an effective framework for vehicle trajectory recognition on ultra-long highways, and designs a network architecture based on DeepSORT and YOLOv5s for efficient multi-vehicle target tracking to quickly obtain trajectories within the visible area. 2.