The training workflow of neural networks can be quite complex, potentially time‐consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video‐based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision‐based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low‐illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data.

中文翻译：

---

用于夜间交通传感的高效计算视频分析


神经网络的训练工作流程可能非常复杂，可能非常耗时，并且需要特定的硬件来完成操作需求。本研究提出了一种基于视频分析的新颖方法，使用安装在道路上的单目交通监控摄像头进行夜间车辆跟踪和车辆体积估计。为了构建这种方法，我们采用基于计算机视觉的算法来检测车辆对象，执行车辆跟踪，并在预定义的检测区域中进行车辆计数。为了解决低照度条件，我们采用图像降噪技术、图像二值转换、图像投影变换和一组启发式推理规则来提取每辆车的前灯，将属于同一车辆的它们配对，并跟踪移动候选车辆对象连续穿过一系列视频帧。使用公开可用的车辆数据集以及自己的标记视频数据在各种场景和环境条件下测试了所提出方法的鲁棒性。