Adaptive human motion tracking using non-synchronous multiple viewpoint observations

We propose an adaptive human tracking system with non-synchronous multiple observations. Our system consists of three types of processes: discovering node for detecting newly appeared person; tracking node for tracking each target person; and observation node for processing one viewpoint (camera) images. We have multiple observation nodes and each node works independently. The tracking node integrates the observed information based on reliability evaluation. Both the observation conditions and human motion states are considered in the evaluation. Matching between tracking models and observed image features are performed in each of the observation node based on the position, size and color similarities of each 2D image. Due to the non-synchronous property, this system is highly scalable for increasing the detection area and number of observing nodes. Experimental results for some indoor scenes are also described