用深度网络分析停车影像，自动估算车辆停留时长。
Using Deep Neural Networks to Quantify Parking Dwell Time
- 先用分类网络判断车位是否被占，再用孪生网络比对车辆是否同一辆。
- 理想情况下，75%的停车时长预测完全准确；真实场景下仍达49%。
- 适合城市交通管理、智慧停车系统研发人员参考。
在智慧城市中，为提升车位周转率并减少私家车使用，通常会对特定停车位设定最长停留时间。然而，从低分辨率摄像头拍摄的图像中自动识别单个车辆的停留时长面临诸多挑战，如图像质量差、光照变化和天气影响。本文提出一种结合两个深度神经网络的方法，用于计算停车场内每辆车的停留时间。首先，通过深度分类网络判断车位状态（占用或空闲）；随后，利用孪生网络检测当前车辆是否与前一时刻相同。在侧重跨数据集场景的实验中，若使用理想分类器，系统可实现75%的完美停留时间预测（预测值与实际停留时间完全一致）。然而，当采用真实世界分类器时，预测准确率下降至49%，表明所提孪生网络虽具潜力，但其性能受初始分类器质量显著影响。
原文摘要 · Abstract (English)
In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transportation solutions. However, automatically determining individual car dwell times from images faces challenges, such as images collected from low-resolution cameras, lighting variations, and weather effects. In this work, we propose a method that combines two deep neural networks to compute the dwell time of each car in a parking lot. The proposed method first defines the parking space status between occupied and empty using a deep classification network. Then, it uses a Siamese network to check if the parked car is the same as the previous image. Using an experimental protocol that focuses on a cross-dataset scenario, we show that if a perfect classifier is used, the proposed system generates 75% of perfect dwell time predictions, where the predicted value matched exactly the time the car stayed parked. Nevertheless, our experiments show a drop in prediction quality when a real-world classifier is used to predict the parking space statuses, reaching 49% of perfect predictions, showing that the proposed Siamese network is promising but impacted by the quality of the classifier used at the beginning of the pipeline.
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