Vehicle search is one basic task for the efficient traffic management in
terms of the AI City. Most existing practices focus on the image-based vehicle
matching, including vehicle re-identification and vehicle tracking. In this
paper, we apply one new modality, i.e., the language description, to search the
vehicle of interest and explore the potential of this task in the real-world
scenario. The natural language-based vehicle search poses one new challenge of
fine-grained understanding of both vision and language modalities. To connect
language and vision, we propose to jointly train the state-of-the-art vision
models with the transformer-based language model in an end-to-end manner.
Except for the network structure design and the training strategy, several
optimization objectives are also re-visited in this work. The qualitative and
quantitative experiments verify the effectiveness of the proposed method. Our
proposed method has achieved the 1st place on the 5th AI City Challenge,
yielding competitive performance 18.69% MRR accuracy on the private test set.
We hope this work can pave the way for the future study on using language
description effectively and efficiently for real-world vehicle retrieval
systems. The code will be available at
this https URL

本研究使用基于语言描述的车辆搜索来探索其在实际场景中的潜力，结合最先进的视觉模型和基于 Transformer 的语言模型，重新审视了网络结构设计、训练策略和优化目标，实验结果显示出了优越的性能，并希望为未来车辆检索系统的研究铺平道路。

自然语言驱动的车辆检索中连接语言与视觉

Connecting Language and Vision for Natural Language-Based Vehicle  Retrieval

Tracking of multiple objects is an important application in AI City geared
towards solving salient problems related to safety and congestion in an urban
environment. Frequent occlusion in traffic surveillance has been a major
problem in this research field. In this challenge, we propose a model-based
vehicle localization method, which builds a kernel at each patch of the 3D
deformable vehicle model and associates them with constraints in 3D space. The
proposed method utilizes shape fitness evaluation besides color information to
track vehicle objects robustly and efficiently. To build 3D car models in a
fully unsupervised manner, we also implement evolutionary camera
self-calibration from tracking of walking humans to automatically compute
camera parameters. Additionally, the segmented foreground masks which are
crucial to 3D modeling and camera self-calibration are adaptively refined by
multiple-kernel feedback from tracking. For object detection/classification,
the state-of-the-art single shot multibox detector (SSD) is adopted to train
and test on the NVIDIA AI City Dataset. To improve the accuracy on categories
with only few objects, like bus, bicycle and motorcycle, we also employ the
pretrained model from YOLO9000 with multi-scale testing. We combine the results
from SSD and YOLO9000 based on ensemble learning. Experiments show that our
proposed tracking system outperforms both state-of-the-art of tracking by
segmentation and tracking by detection.

本文提出了一种基于 3D 变形车辆模型的车辆定位方法，通过使用形状适配度评估以及颜色信息来跟踪车辆对象，结合了 SSD 和 YOLO9000 两种先进的算法实现车辆的检测和分类，并利用集成学习技术来提高分类的准确性，实验结果表明该方法性能优于当前领先的跟踪算法。同时，该方法还实现了自动化的相机自标定过程。