In this paper, an advanced-and-reliable vehicle detection-and-tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and-Tracking (RT_VDT) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The RT_VDT is mainly apipeline of reliable computer vision and machine learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main contribution of this paper is the careful fusion of the employed algorithms where some of them work in parallel to strengthen each other in order to produce a precise and sophisticated real-time output. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs that are currently employed by ADAS systems. The particulars of the employed algorithms together with their implementation are described in detail. Additionally, these algorithms and their various integration combinations are tested and their performance is evaluated using actual road images, and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark with 87% average precision achieved. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions.

中文翻译：

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用于高级驾驶辅助系统的轻型车辆检测和跟踪技术

本文提出并实现了一种先进可靠的车辆检测跟踪技术。实时车辆检测和跟踪（RT_VDT）技术非常适合高级驾驶辅助系统（ADAS）应用或无人驾驶汽车（SDC）。RT_VDT主要是可靠的计算机视觉和机器学习算法的流水线，它们相互补充并获取原始RGB图像，以产生出现在汽车前部驾驶空间中的所需车辆边界框。本文的主要贡献是对所采用算法的仔细融合，其中一些算法并行工作以相互增强，以产生精确而复杂的实时输出。此外，RT_VDT提供了足够快的计算能力，可以嵌入到ADAS系统当前使用的CPU中。详细描述了所采用算法的细节及其实现。此外，还使用实际的道路图像对这些算法及其各种集成组合进行了测试，并评估了它们的性能，并通过汽车的前置摄像头以及在KITTI基准上捕获的视频实现了87％的平均精度。对RT_VDT的评估表明，它可以在各种情况下可靠地检测和跟踪车辆边界。以及通过车载前置摄像头和KITTI基准拍摄的视频，平均精度达到87％。对RT_VDT的评估表明，它可以在各种情况下可靠地检测和跟踪车辆边界。以及通过车载前置摄像头和KITTI基准拍摄的视频，平均精度达到87％。对RT_VDT的评估表明，它可以在各种情况下可靠地检测和跟踪车辆边界。