Target recognizing of unmanned aerial vehicle (UAV) based on image processing take the advantage of 2D information containing in the image for identifying the target. Compare to single UAV with electrical optical tracking system (EOTS), multi-UAV with EOTS is able to take a group of image focused on the suspected target from multiple view point. Benefit from matching each couple of image in this group, points set constituted by matched feature points implicates the depth of each point. Coordinate of target feature points could be computing from depth of feature points. This depth information makes up a cloud of points and reconstructed an exclusive 3D model to recognizing system. Considering the target recognizing do not require precise target model, the cloud of feature points was regrouped into n subsets and reconstructed to a semi-3D model. Casting these subsets in a Cartesian coordinate and applying these projections in convolutional neural networks (CNN) respectively, the integrated output of networks is the improved result of recognizing.
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