Abstract
Extracting vehicle information from surveillance images is essential for intelligent transportation systems, enabling applications such as traffic monitoring and criminal investigations. While Automatic License Plate Recognition (ALPR) is widely used, Fine-Grained Vehicle Classification (FGVC) offers a complementary approach by identifying vehicles based on attributes such as color, make, model, and type. Although there have been advances in this field, existing studies often assume well-controlled conditions, explore limited attributes, and overlook FGVC integration with ALPR. To address these gaps, we introduce UFPR-VeSV, a dataset comprising 24,945 images of 16,297 unique vehicles with annotations for 13 colors, 26 makes, 136 models, and 14 types. Collected from the Military Police of Paraná (Brazil) surveillance system, the dataset captures diverse real-world conditions, including partial occlusions, nighttime infrared imaging, and varying lighting. All FGVC annotations were validated using license plate information, with text and corner annotations also being provided. A qualitative and quantitative comparison with established datasets confirmed the challenging nature of our dataset. A benchmark using five deep learning models further validated this, revealing specific challenges such as handling multicolored vehicles, infrared images, and distinguishing between vehicle models that share a common platform. Additionally, we apply two optical character recognition models to license plate recognition and explore the joint use of FGVC and ALPR. The results highlight the potential of integrating these complementary tasks for real-world applications. The UFPR-VeSV dataset is publicly available at: this https URL.
Abstract (translated)
从监控图像中提取车辆信息对于智能交通系统至关重要，可支持交通监控和刑事调查等应用。虽然自动车牌识别（ALPR）已广泛应用，但细粒度车辆分类（FGVC）通过识别颜色、品牌、型号和类型等属性提供了互补方法。尽管该领域已有进展，现有研究常假设条件受控、探索的属性有限，且忽视了FGVC与ALPR的整合。为弥补这些不足，我们推出UFPR-VeSV数据集，该数据集包含16,297辆独特车辆的24,945张图像，标注了13种颜色、26个品牌、136种型号和14种类型。数据集源自巴西巴拉那州军事警察监控系统，涵盖了多种真实世界场景，包括部分遮挡、夜间红外成像和光照变化。所有FGVC标注均通过车牌信息验证，同时提供了文本和角点标注。与现有数据集的定性和定量比较证实了本数据集的挑战性。使用五种深度学习模型的基准测试进一步验证了这一点，揭示了处理多色车辆、红外图像以及区分同平台车型等具体挑战。此外，我们应用两种光学字符识别模型进行车牌识别，并探索了FGVC与ALPR的联合使用。结果凸显了整合这些互补任务在现实应用中的潜力。UFPR-VeSV数据集已公开提供，地址为：此https链接。
URL
https://arxiv.org/abs/2604.05271
