Traffic Video Event Retrieval via Text Query Using Vehicle Appearance and Motion Attributes

Tien-Phat Nguyen, Ba-Thinh Tran-Le, Xuan-Dang Thai, Tam V. Nguyen, Minh N. Do, Minh-Triet Tran; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 4165-4172

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.

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[bibtex]
@InProceedings{Nguyen_2021_CVPR, 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}, title = {Traffic Video Event Retrieval via Text Query Using Vehicle Appearance and Motion Attributes}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {4165-4172} }