Video-based trajectory extraction with deep learning for High-Granularity Highway Simulation(HIGH-SIM)

High-granularity vehicle trajectory data can help researchers develop traffic simulation models,understand traffic flow characteristics,and thus propose insightful strategies for road traffic management.This paper proposes a novel vehicle trajectory extraction method that can extract high-granularity vehicle trajectories from aerial videos.The proposed method includes video calibration,vehicle detection and tracking,lane marking identification,and vehicle motion characteristics calculation.In particular,the authors propose a Monte-Carlo-based lane marking identification approach to identify each vehicle's lane.This is a challenging problem for vehicle trajectory extraction,especially when the aerial videos are taken from a high altitude.The authors applied the proposed method to extract vehicle trajectories from several high-resolution aerial videos recorded from helicopters.The extracted dataset is named by the High-Granularity Highway Simulation(HIGH-SIM)vehicle trajectory dataset.To demonstrate the effectiveness of the proposed method and understand the quality of the HIGHSIM dataset,we compared the HIGH-SIM dataset with a well-known dataset,the NGSIM US-101 dataset,regarding the accuracy and consistency aspects.The comparison results showed that the HIGH-SIM dataset has more reasonable speed and acceleration distributions than the NGSIM US-101 dataset.Also,the internal and platoon consistencies of the HIGH-SIM dataset give lower errors compared to the NGSIM US-101 dataset.To benefit future research,the authors have published the HIGH-SIM dataset online for public use.

Novel learning framework for optimal multi-object video trajectory tracking

Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. Methods To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky-Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. Results In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Conclusions Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.

A mesoscale eddy detection method of specific intensity and scale from SSH image in the South China Sea and the Northwest Pacific

Mesoscale eddies exist almost everywhere in the ocean and play important roles in the ocean circulation of the world. These eddies may cause sound spread singular regions and bring great influences to the upwater ship and underwater aircraft. Due to the lack of hydrographic survey datasets, study of mesoscale eddies has been greatly restricted. Fortunately, satellite altimeter provided an effective way to study mesoscale eddies. An automatic detection algorithm is introduced to detect mesoscale eddies of specific intensity and spatial/temporal scale based on satellite sea surface height(SSH) data and the algorithm is applied in a strong eddy activity region: the South China Sea and the Northwest Pacific. The algorithm includes four steps. The first step is preprocessing of the SSH image, which includes elimination of error SSH data and interpolation. The second step is to detect suspected mesoscale eddies from preprocessed SSH images by dynamic threshold adjustment and morphological method, and the suspected mesoscale eddy detection includes two procedures: suspected mesoscale eddy core region detection and suspected mesoscale eddy brim extraction. The third step is to pick out mesoscale eddies satisfied with specified criteria from suspected mesoscale eddies. The criteria include three items, that is, intensity criterion, spatial scale, criterion and temporal scale criterion. The last step is algorithm performance analysis and verification. The algorithm has the capability of adaptive parameter adjustment, and can extract mesoscale eddies of interested intensity and spatial/temporal scale. The paper can provide a basis for analyzing space-time characteristics of mesoscale eddy in the South China Sea and the Northwest Pacific.