面向网约车市场分析的粗到精目标检测

Alvin Prayuda Juniarta Dwiyantoro, K. Muchtar, Faris Rahman, Muhammad Wiryahardiyanto, Reynaldy Hardiyanto
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引用次数: 0

摘要

迄今为止,网约车服务已经开发了一个以客户为中心的平台,为客户提供积极的体验。在本文中,我们提出了计算机视觉技术,以提取市场洞察,通过综合监控系统。具体来说,我们将100条路线上的网约车服务的司机根据他们的公司进行实时分类。设计实时分类系统面临两个主要挑战:(1)两类驾驶员之间的视觉外观几乎相似;(2)每类的样本分布不平衡。为了克服这些问题,在本文中,我们介绍了在分类驾驶员的背景下使用粗到精的方法。我们将我们的方法分为两个主要部分;弱目标检测和细化分类。正如在实验部分所全面评估的那样,我们的方法可以用于分析CCTV数据流,具有高效率和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coarse-to-Fine Object Detection for Ride-Hailing Market Analysis
To date, ride-hail services have developed a customer-centric platform to provide a positive experience for their customers. In this paper, we propose the computer vision techniques to extract market insight through integrated surveillance systems. To be specific, we classify the driver of ride-hail services that travel in a hundred routes according to their company in real-time. There are two major challenges to designing a real-time classification system: (1) almost similar in visual appearance between two classes of drivers, and (2) unbalanced sample distribution per class. In order to overcome these problems, in this paper, we introduce the use of the coarse-to-fine approach in the context of classifying drivers. We separate our approach into two main parts; weak object detection and refinement classification, respectively. As thoroughly evaluated in the experimental section, our approach can be used to analyze the CCTV data streams with high efficiency and robustness.
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