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The nature of multiple samples to extract correlation information limits theapplications of ghost imaging of moving objects. A novel multi-to-one neuralnetwork is proposed and the concept of "batch frame" is introduced to improvethe serial imaging method. The neural network extracts more correlationinformation from a small number of samples, thus reducing the sampling ratio ofthe ghost imaging technique. We combine the correlation characteristics betweenimages to propose a frame merging algorithm, which eliminates the dynamic blurof high-speed moving objects and further improves the reconstruction quality ofmoving object images at a low sampling ratio. The experimental results areconsistent with the simulation results.

Group frame neural network of moving object ghost imaging combined with frame merging algorithm