Adaptive multi-vehicle motion counting
Adaptive multi-vehicle motion counting
复制标题
DOI：
10.1007/s11760-022-02184-5
复制
发表时间：
2022-04
期刊：
影响因子：
--
通讯作者：
Xuan-Duong Nguyen;Anh-Khoa Nguyen Vu;Thanh-Danh Nguyen;Nguyen-Minh-Thao Phan;Bao-Duy Duyen Dinh;Nhat-Duy Nguyen;Tam V. Nguyen;Vinh-Tiep Nguyen;Duy-Dinh Le
中科院分区：
文献类型：
--
作者：
Xuan-Duong Nguyen;Anh-Khoa Nguyen Vu;Thanh-Danh Nguyen;Nguyen-Minh-Thao Phan;Bao-Duy Duyen Dinh;Nhat-Duy Nguyen;Tam V. Nguyen;Vinh-Tiep Nguyen;Duy-Dinh Le
通过城市地区的交通摄像头计算多辆车的运动对智慧城市至关重要。尽管在这项任务中已经提出了几个框架，但没有以前的工作集中在高度常见的，密集的和大小可变的车辆，如摩托车。在本文中，我们提出了一种新的框架，车辆运动计数与自适应标签无关的跟踪和计数模块，每秒处理12帧。我们的框架适用于多车辆跟踪的超参数，并在复杂的交通条件下正常工作，特别是对摄像机视角不变。在均方根误差和运行时性能方面，我们取得了有竞争力的结果。
Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptive label-independent tracking and counting modules that processes 12 frames per second. Our framework adapts hyperparameters for multi-vehicle tracking and properly works in complex traffic conditions, especially invariant to camera perspectives. We achieved the competitive results in terms of root-mean-square error and runtime performance.