@inproceedings{bibcite_851,
  author = {Tien-Phat Nguyen and Ba-Thinh Tran-Le and Xuan-Dang Thai and Tam Nguyen and Minh Do and Minh-Triet Tran},
  title = {Traffic Video Event Retrieval via Text Query using Vehicle Appearance and Motion Attributes},
  abstract = {Traffic event retrieval is one of the important tasks for intelligent traffic system management. To find accurate candidate events in traffic videos corresponding to a specific text query, it is necessary to understand the text query{\textquoteright}s attributes, represent the visual and motion attributes of vehicles in videos, and measure the similarity between them. Thus we propose a promising method for vehicle event retrieval from a natural-language-based specification. We utilize both appearance and motion attributes of a vehicle and adapt the COOT model to evaluate the semantic relationship between a query and a video track. Experiments with the test dataset of Track 5 in AI City Challenge 2021 show that our method is among the top 6 with a score of 0.1560.},
  year = {2021},
  journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops},
  month = {06},
  issn = {2160-7516},
  url = {https://par.nsf.gov/biblio/10277269},
}