A recognition framework based on common cause detection in multimodal signals

为了在真实环境中实现人和物体的识别，我们提出了一种新的基于人的感知方式的多传感器融合方法。以往的传感器集成方法是将不同类型的传感器经过特征提取和提取(“识别后集成”)组合在一起。我们在前期提出了一种融合不同类型传感器的感知信号的新方法，并将其应用于人的识别和跟踪问题。利用激光测距仪(LRF)跟踪环境中行人的腿部，同时观测到行人的加速度信号。基于本文提出的基于局部步行节奏的信号关联方法，对来自同一行人的信号进行关联。我们已经在Int上展示了这项研究。电话会议在《普及与嵌入式计算与通信系统》(PECCS 2012，论文全文接受率17%)上获奖，并获得最佳论文奖。

To realize people and object recognition in real environment, we have proposed new multi sensor integration approach in the manner of human perception. Previous approaches of sensor integration combined different kinds of sensors after feature extraction and abstraction("integration after recognition"). We have proposed a new approach that combines sensory signals from different kinds of sensors in the earlier stage, and applied it to the problem of people identification and tracking. The legs of pedestrians in the environment are tracked by using laser range finders(LRFs), and acceleration signals from pedestrians are simultaneously observed. We associate these signals from same pedestrian based on the proposed signal correlation method that focuses on local waling rhythm. We has presented this study at the Int. Conf. on Pervasive and Embedded Computing and Communication Systems(PECCS 2012, full paper acceptance ratio 17%) and obtained the best paper award.