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首页> 外文期刊>Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic >Passenger flow counting in buses based on deep learning using surveillance video
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Passenger flow counting in buses based on deep learning using surveillance video

机译：基于深入学习的公共汽车使用监控视频计算乘客流量

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摘要

An efficient traffic management system is crucial for public transportation. If the passenger flow can be detected accurately and instantaneously, the routes and schedules for public transportation can be effectively improved. However, previous research identified many challenges in passenger counting, such as messy image background, variations in lighting, and occlusions. In this paper, we propose a passenger flow counting model for buses, based on deep learning. First, we design a straightforward way to understand the opening state of the door. Next, a single shot multibox detector is used to learn the features of passengers and detect them. Finally, a particle filter with a three-step cascaded data association scheme is used for passenger tracking. To demonstrate the performance of the proposed algorithm, surveillance videos of three different situations, i.e., day, night, and a rainy day, are selected. Additionally, to make the system applicable to real cases, a few special scenes such as different objects worn by the passengers, passenger occlusions, and dense crowds, are considered. According to the experimental results, our method exhibits better performance than some existing methods.
机译：有效的交通管理系统对于公共交通至关重要。如果可以准确且瞬间检测乘客流量，可以有效地改善公共交通的路线和时间表。然而，之前的研究确定了乘客计数中的许多挑战，例如凌乱的图像背景，照明和闭塞的变化。在本文中，我们提出了一种基于深度学习的公共汽车的乘客流量计数模型。首先，我们设计了理解门的开口状态的直接方式。接下来，用于学习乘客的特征并检测它们的单次拍摄。最后，使用具有三步级联数据关联方案的粒子滤波器用于乘客跟踪。为了展示所提出的算法的表现，选择了三种不同情况的监视视频，即日，夜晚和下雨天。此外，为了使系统适用于实际情况，考虑了乘客，乘客闭塞和密集人群佩戴的不同物体等特殊场景。根据实验结果，我们的方法表现出比现有方法更好的性能。

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