As Computer Vision technologies become more mature for intelligent transportation applications, it is time to ask how efficient and scalable they are for large-scale and real-time deployment. Among these technologies is Vehicle Re-Identification which is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. To balance the demands of accuracy and computational efficiency, in this work we propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time and is free of intricate and computation-demanding add-on modules often seen in state-of-the-art approaches. Through extensive experiments, we show our approach, termed Self-Supervised and Boosted VEhicle Re-Identification (SSBVER), is on par with state-of-the-art alternatives in terms of accuracy without introducing any additional overhead during deployment. Additionally we show that our approach, generalizes to different backbone architectures which facilitates various resource constraints and consistently results in a significant accuracy boost.

翻译：随着计算机视野技术在智能运输应用方面变得更加成熟,现在应该问一问,这些技术在大规模和实时部署方面的效率和可扩展性有多高。这些技术包括车辆再识别,这是城市规模车辆分析系统的关键要素之一。许多最先进的车辆再定位解决方案大多侧重于提高现有重设基准的准确性,而且往往忽视计算的复杂性。为了平衡准确性和计算效率的要求,我们在此工作中提出了一个简单而有效的混合解决方案,通过自我监督培训增强能力,这种培训只在推断时使用单一的网络,而且没有最先进的方法中常见的复杂和计算需求附加模块。通过广泛的实验,我们展示了我们的方法,即“自我强化和推进的车辆再识别”(SSBVVER),在准确性方面与最先进的替代方法相近,在部署期间不引入任何额外的间接费用。此外,我们表明,我们的方法是向不同的主干结构普及,这种结构有助于各种资源限制和持续地提高精确性。