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第三十九届中国控制会议

作者： Xinghui Li Meiqin Liu Senlin Zhang Ronghao Zheng State Key Laboratory of Industrial Control Technology Zhejiang University College of Electrical Engineering Zhejiang University

Extracting fish trajectories from underwater videos is an important way to analyze fish behavior and can provide guidance for aquaculture. Most of the existing researches are based on traditional image processing algorithms and CamShift tracking algorithm. In recent years, convolutional neural networks have performed better than traditional image processing algorithms in many computer vision tasks. This paper proposes a fish trajectory extraction method based on object detection. First, the deep learning object detection model Faster RCNN is used to predict fish for all video frames. Then based on the criteria of IoU, center distance and probability similarity, a greedy algorithm or Hungarian algorithm is used to correlate the prediction results. Finally, improvements are done from two aspects of linking and deleting. Experiments show that this method can well complete the task of fish trajectory extraction, and the AP performance is increased from 74.75% to 80.94%.

关键词： trajectory extraction Object Detection Video Processing

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Novel learning framework for optimal multi-object video trajectory tracking

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虚拟现实与智能硬件(中英文) 2023年第5期5卷 422-438页

作者： Siyuan CHEN Xiaowu HU Wenying JIANG Wen ZHOU Xintao DING School of Computer and Information Anhui Normal UniversityAnhui 241002China

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.

关键词： Web3D Virtual evacuation Multi-object tracking trajectory extraction trajectory optimization

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维普期刊数据库

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

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Science China Earth Sciences 2014年第8期57卷 1897-1906页

作者： ZHANG ChunHua XI XiaoLiang LIU SongTao SHAO LianJun HU XiaoHua No. 107 Beiqing Road Haidian District The 7th Department Navy Aeronautical Engineering University

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.

关键词： mesoscale eddy automatic detection sea surface height (SSH) connected component label trajectory extraction

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