Natural language (NL) based vehicle retrieval is a task aiming to retrieve a
vehicle that is most consistent with a given NL query from among all candidate
vehicles. Because NL query can be easily obtained, such a task has a promising
prospect in building an interactive intelligent traffic system (ITS). Current
solutions mainly focus on extracting both text and image features and mapping
them to the same latent space to compare the similarity. However, existing
methods usually use dependency analysis or semantic role-labelling techniques
to find keywords related to vehicle attributes. These techniques may require a
lot of pre-processing and post-processing work, and also suffer from extracting
the wrong keyword when the NL query is complex. To tackle these problems and
simplify, we borrow the idea from named entity recognition (NER) and construct
FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL
descriptions of vehicle tracks, containing information such as the location,
orientation, type and colour of the vehicle. FindVehicle also adopts both
overlapping entities and fine-grained entities to meet further requirements. To
verify its effectiveness, we propose a baseline NL-based vehicle retrieval
model called VehicleFinder. Our experiment shows that by using text encoders
pre-trained by FindVehicle, VehicleFinder achieves 87.7\% precision and 89.4\%
recall when retrieving a target vehicle by text command on our homemade dataset
based on UA-DETRAC. The time cost of VehicleFinder is 279.35 ms on one ARM v8.2
CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the
Transformer-based system. The dataset is open-source via the link
this https URL, and the implementation can be found
via the link this https URL

本研究针对提取车辆属性相关关键词时所需大量预处理和后处理工作的问题，借鉴命名实体识别 (NER) 技术构建了一个包含 42.3k 车辆跟踪信息标注的 NL 数据集 - FindVehicle, 通过使用由 FindVehicle 预训练的文本编码器，我们提出了基于自然语言的车辆检索模型 VehicleFinder, 在 homemade 数据集上实现了 87.7% 精度和 89.4% 召回率，每次查询时间为 279.35 毫秒到 93.72 毫秒之间，比基于 Transformer 的系统快得多.