A recognition framework based on common cause detection in multimodal signals

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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.