Abstract
Multiple object trajectory extraction from drone-based videos is a challenging problem due to the dynamic background. In this paper, we propose a novel method to extract small object trajectories and identify group movement from drone-based videos. A deep learning-based feature point extractor is used to extract features from video frames. The points that represent the features are tracked in frame sequence and joined to generate trajectories. To obtain the actual trajectories, we introduce a strategy to model the background movement and remove it from point-connected trajectories. Furthermore, we identify multiple objects into groups based on trajectory similarity and object class. Experiments on the public drone-based videos demonstrate that our method can effectively extract group object trajectories and avoid the distortion of background movement.
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This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61876187.
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Ma, A., Zhou, H., Ma, Z., Li, X., Niu, Y. (2023). Group Object Trajectories Extraction in Drone-Based Videos with Dynamic Background. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_428
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DOI: https://doi.org/10.1007/978-981-19-6613-2_428
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