@inproceedings{2c4b91c9218144f6a07d9d06e2366413,
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'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.",
author = "Nguyen, {Tien Phat} and Tran-Le, {Ba Thinh} and Thai, {Xuan Dang} and Nguyen, {Tam V.} and Do, {Minh N.} and Tran, {Minh Triet}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 ; Conference date: 19-06-2021 Through 25-06-2021",
year = "2021",
month = jun,
doi = "10.1109/CVPRW53098.2021.00470",
language = "English (US)",
series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE Computer Society",
pages = "4160--4167",
booktitle = "Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021",
}