Efficient CityCam-to-Edge Cooperative Learning for Vehicle Counting in ITS
Efficient CityCam-to-Edge Cooperative Learning for Vehicle Counting in ITS
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DOI：
10.1109/tits.2022.3149657
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发表时间：
2022-09
影响因子：
8.5
通讯作者：
Honghui Xu;Zhipeng Cai;Ruinian Li;Wei Li
中科院分区：
文献类型：
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作者：
Honghui Xu;Zhipeng Cai;Ruinian Li;Wei Li
车辆计数是智能交通系统（ITS）中用于城市交通管理的重要组成部分。尽管已经提出了许多车辆计数方法，但它们的本质缺陷限制了车辆计数在真实的应用中的功效。在本文中，我们提出了一个CityCam-to-Edge协作学习框架，通过将多个城市摄像机与边缘服务器合作来更有效地计数车辆。我们的学习框架由部署在城市摄像头上的轻量级特征提取方案和在边缘服务器上实现的车辆计数模型组成。在深度学习架构的设计中，我们设计了轻量级的特征提取方案，利用多个卷积层和较少的内核，减少了特征提取参数的使用，从而大大降低了城市摄像头的内存消耗和数据传输时间。此外，我们设计了两个新的车辆计数模型，F2 F-M和O2O-M，分别利用从多个城市摄像机捕获的视频之间的时间相关性，以帧到帧的方式和视频到视频的方式，以提高计数性能。通过结合轻量级特征提取方案和提出的车辆计数模型，我们得到了两个端到端的车辆计数模型，Lite-F2 F-M和Lite-O2O-M。最后，通过大量的实验，我们证明了Lite-F2 F-M和Lite-O2O-M模型在车辆计数精度和时间效率方面优于最先进的模型。
Vehicle counting is a fundamental component in Intelligent Transportation System (ITS) for city traffic management. Although a number of vehicle counting approaches have been proposed, their essential drawbacks limit the efficacy of vehicle counting in real applications. In this paper, we propose a CityCam-to-Edge cooperative learning framework by cooperating multiple city cameras with an edge server to count vehicles more efficiently. Our learning framework consists of a lightweight feature extraction scheme deployed on the city cameras and a vehicle counting model implemented on the edge server. We devise the lightweight feature extraction scheme by leveraging multiple convolutional layers with few kernels in the design of deep learning architecture to reduce the utilization of parameters for feature extraction, so that the city cameras’ memory consumption and the data transmission time can be greatly reduced. Moreover, we design two novel vehicle counting models, F2F-M and O2O-M, to improve the counting performance by exploiting the temporal correlation among videos captured from multiple city cameras in a frame-to-frame manner and a video-to-video manner, respectively. By combining the lightweight feature extraction scheme and the proposed vehicle counting models, we obtain two end-to-end vehicle counting models, Lite-F2F-M and Lite-O2O-M. Finally, via conducting extensive experiments, we demonstrate that Lite-F2F-M and Lite-O2O-M models outperform the state-of-the-art in terms of vehicle counting accuracy and time efficiency.