@article{829,
  author = {Xuan-Duong Nguyen and Anh-Khoa Vu and Thanh-Danh Nguyen and Nguyen Phan and Bao-Duy Dinh and Nhat-Duy Nguyen and Tam Nguyen and Vinh-Tiep Nguyen and Duy-Dinh Le},
  title = {Adaptive multi-vehicle motion counting},
  abstract = {Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptive label-independent tracking and counting modules that processes 12 frames per second. Our framework adapts hyperparameters for multi-vehicle tracking and properly works in complex traffic conditions, especially invariant to camera perspectives. We achieved the competitive results in terms of root-mean-square error and runtime performance.},
  year = {2022},
  journal = {Signal, Image and Video Processing},
  month = {04},
  issn = {1863-1703},
  url = {https://par.nsf.gov/biblio/10330090},
  doi = {10.1007/s11760-022-02184-5},
}