  Visual perception is an effective way to obtain the spatial characteristics
of wireless channels and to reduce the overhead for communications system. A
critical problem for the visual assistance is that the communications system
needs to match the radio signal with the visual information of the
corresponding user, i.e., to identify the visual user that corresponds to the
target radio signal from all the environmental objects. In this paper, we
propose a user matching method for environment with a variable number of
objects. Specifically, we apply 3D detection to extract all the environmental
objects from the images taken by multiple cameras. Then, we design a deep
neural network (DNN) to estimate the location distribution of users by the
images and beam pairs at multiple moments, and thereby identify the users from
all the extracted environmental objects. Moreover, we present a resource
allocation method based on the taken images to reduce the time and spectrum
overhead compared to traditional resource allocation methods. Simulation
results show that the proposed user matching method outperforms the existing
methods, and the proposed resource allocation method can achieve $92\%$
transmission rate of the traditional resource allocation method but with the
time and spectrum overhead significantly reduced.
