Person detection is often critical for personal safety, property protection, and national security. Most person detection technologies implement unimodal classification, making predictions based on a single sensor data modality, which is most often vision. There are many ways to defeat unimodal person detectors, and many more reasons to ensure technologies responsible for detecting the presence of a person are accurate and precise. In this paper, we design and implement a multimodal person detection system which can acquire data from multiple sensors and detect persons based on a variety of unimodal classifications and multimodal fusions. We present two methods of generating system-level predictions: (1) device perspectives which makes a final decision based on multiple device-level predictions and (2) system perspectives which combines data samples from multiple devices into a single data sample and then makes a decision. Our experimental results show that system-level predictions from system perspectives are generally more accurate than system-level predictions from device perspectives. We achieve an accuracy of 100%, zero false positive rate and zero false negative rate with fusion of system perspectives motion and distance data.

人体检测通常对于人身安全，财产保护和国家安全至关重要。大多数人检测技术都实现单峰分类，从而基于单个传感器数据形式（通常是视觉）进行预测。有许多方法可以击败单峰人体检测器，还有更多的理由来确保负责检测人体存在的技术是准确而精确的。在本文中，我们设计并实现了一种多模式人员检测系统，该系统可以从多个传感器获取数据并基于各种单模式分类和多模式融合来检测人员。我们介绍了两种生成系统级预测的方法：（1）基于多个设备级别预测做出最终决策的设备角度，以及（2）将来自多个设备的数据样本组合为单个数据样本然后做出决定的系统角度。我们的实验结果表明，从系统角度进行系统级预测通常比从设备角度进行系统级预测更为准确。通过融合系统透视运动和距离数据，我们达到了100％的准确度，零误报率和零误报率。