Multi-source heterogeneous data fusion method and its application in object positioning and tracking
The general sensing of Internet of Things (IoT) brings magnanimous sensing data,which shows significant multi-source heterogeneous property.How to process the multi-sourceheterogeneous sensing data intelligently and efficiently is a challenging problem.Although data fusion is considered an effective approach to processing multi-modal data and extract the hiding valuable information,there are many problems to be solved for multi-source heterogeneous data fusion,especially the unstructured video multimedia information.In this paper,the multi-source heterogeneous data fusion problem is explored and a multi-level fusion method is proposed and applied in object positioning and tracking using wireless signal,video and depth data.In the proposed method,the main opponents of data fusion are deeply studied,including the processing methods of different types of data,the feature representation and different level data fusion methods.For different types of data,from their inherent characteristics,different fusion methods at different levels are adopted here to mine the correlative relations and derive the valuable information of the heterogeneous data.The proposed method is evaluated by the object tracking and positioning experiments in actual complicated scenarios.The results show that the proposed multi-source heterogeneous data fusion based method can solve the difficulties in the traditional single dada based tracking method,such as the illumination variation,occlusion and the clutter of background.Additionally,the proposed method can estimate the three-dimensional position of the tracking object with high accuracy.
multi-source heterogeneousinformation fusiontracking and positioningfeature representationInternet of Things
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