论文标题
朝着智能摄像机停车系统的无错误的深度居住探测器
Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System
论文作者
论文摘要
尽管智能摄像机停车系统概念已经存在数十年，但一些方法已经完全解决了该系统的可扩展性和可靠性。由于智能停车系统的基石是检测占用率的能力，因此传统方法使用分类骨干来预测手动标记网格的斑点。这是耗时的，失去了系统的可扩展性。此外，大多数方法都使用深度学习模型，使其并非没有错误，也不可靠。因此，我们提出了一个端到端的智能相机停车系统，在该系统中，我们提供了一个称为OCPDET的对象检测器的自主检测占用。我们的探测器还提供了对比模块的有意义的信息：训练和空间知识，这些知识避免了推理期间的错误检测。我们在现有的PKLOT数据集上基于OCPDET进行基准OCPDET，并与传统的分类解决方案相比达到竞争结果。我们还引入了一个额外的SNU-SPS数据集，其中我们从各种视图中估算了系统性能，并在停车任务中进行系统评估。我们数据集的结果表明，我们的系统对现实世界应用有望。
Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability. As the cornerstone of a smart parking system is the ability to detect occupancy, traditional methods use the classification backbone to predict spots from a manual labeled grid. This is time-consuming and loses the system's scalability. Additionally, most of the approaches use deep learning models, making them not error-free and not reliable at scale. Thus, we propose an end-to-end smart camera parking system where we provide an autonomous detecting occupancy by an object detector called OcpDet. Our detector also provides meaningful information from contrastive modules: training and spatial knowledge, which avert false detections during inference. We benchmark OcpDet on the existing PKLot dataset and reach competitive results compared to traditional classification solutions. We also introduce an additional SNU-SPS dataset, in which we estimate the system performance from various views and conduct system evaluation in parking assignment tasks. The result from our dataset shows that our system is promising for real-world applications.
