Object recognition in stereo sequences is a simulation of human visual systems on how to analyze and understand various scenes. A pair of stereo sequences is a type of complicated information with huge amount of raw data and features associated with different parameter spaces. Therefore the automatic object recognition in stereo sequences is a difficult and unsolved task challenging many researchers. This paper puts its emphasis on solving this problem based on the data fusion theory. A new algorithm is proposed and experimented on real data. The results show that the accuracy of the object recognition is improved by applying the fusion of both 3D and motion parameters.
['Yi Wei', 'Chunyan Tan', 'Wei Wang']
Data Fusion and its Application in Object Recognition in Stereo Sequences