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Retrieving event videos based on textual description is a promising research topic in the fast-growing data field. Since traffic data increases every day, there is an essential need of an intelligent traffic system to speed up the traffic event search. We propose a multi-module system that outputs accurate results. Our solution considers neighboring entities related to the mentioned object to represent an event by rule-based, which can represent an event by the relationship of multiple objects. We also propose to add a modified model from last year's Alibaba model with an explainable architecture. As the traffic data is vehicle-centric, we apply two language and image modules to analyze the input data and obtain the global properties of the context and the internal attributes of the vehicle. We introduce a one-on-one dual training strategy for each representation vector to optimize the interior features for the query. Finally, a refinement module gathers previous results to enhance the final retrieval result. We benchmarked our approach on the data of the AI City Challenge 2022 and obtained the competitive results at an MMR of 0.3611. We were ranked in the top 4 on 50\% of the test set and in the top 5 on the full set. 

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.