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Natural Language (NL) descriptions can be one of the most convenient or theonly way to interact with systems built to understand and detect city scaletraffic patterns and vehicle-related events. In this paper, we extend thewidely adopted CityFlow Benchmark with NL descriptions for vehicle targets andintroduce the CityFlow-NL Benchmark. The CityFlow-NL contains more than 5,000unique and precise NL descriptions of vehicle targets, making it the firstmulti-target multi-camera tracking with NL descriptions dataset to ourknowledge. Moreover, the dataset facilitates research at the intersection ofmulti-object tracking, retrieval by NL descriptions, and temporal localizationof events. In this paper, we focus on two foundational tasks: the VehicleRetrieval by NL task and the Vehicle Tracking by NL task, which take advantageof the proposed CityFlow-NL benchmark and provide a strong basis for futureresearch on the multi-target multi-camera tracking by NL description task.

CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions