Left-behind humans inside the car or bus have caused a lot of accidents, so it is essential to detect the humans in vehicle. Current human detection methods rely on wearable devices, oxygen sensors, and special seat designs in vehicles, but those sensors cannot adapt to ever-changing environments. To solve those problems and especially to improve passengers’ safety on the bus, we propose a method to accomplishing human detection by fusion vision and microwave radar information in various environments in vehicle. For vision information, we use different networks to extract human and human face features, and fusion of the detection results in different models to improve human detection accuracy. The human detection model is MobileNet-V2, and the human face detection model is MTCNN. A new matching schedule and tracking objects management rule based on the Kernelized Correlation Filter tracker are designed to track the human and human face detection boxes. The microwave radar information is used to detect moving objects. Finally, the fusion vision and microwave radar detection results are implemented. Experiments show that our method has improved the human detection accuracy in vehicle, and this method can be used for detection of left-behind children on the school bus.

中文翻译：

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公交车内多模型融合微波雷达基于视觉的留守人员检测跟踪系统

汽车或公交车内留守人员造成了很多事故，因此检测车辆内的人员至关重要。当前的人体检测方法依赖于可穿戴设备、氧气传感器和车辆中的特殊座椅设计，但这些传感器无法适应不断变化的环境。为了解决这些问题，特别是为了提高乘客在公交车上的安全性，我们提出了一种在车辆的各种环境中通过融合视觉和微波雷达信息来完成人体检测的方法。对于视觉信息，我们使用不同的网络来提取人和人脸特征，并融合不同模型中的检测结果，以提高人体检测的准确性。人体检测模型为MobileNet-V2，人脸检测模型为MTCNN。设计了一种基于核化相关过滤器跟踪器的新匹配计划和跟踪对象管理规则，用于跟踪人和人脸检测框。微波雷达信息用于检测移动物体。最后，实现融合视觉和微波雷达检测结果。实验表明，我们的方法提高了车辆中人体检测的准确性，该方法可用于校车上留守儿童的检测。