  Objects classification generally relies on image acquisition and analysis.
Real-time classification of high-speed moving objects is challenging, as both
high temporal resolution in image acquisition and low computational complexity
in objects classification algorithms are required. Here we propose and
experimentally demonstrate an approach for real-time moving objects
classification without image acquisition. As objects classification algorithms
rely on the feature information of objects, we propose to use spatial light
modulation to acquire the feature information directly rather than performing
image acquisition followed by features extraction. A convolutional neural
network is designed and trained to learn the spatial features of the target
objects. The trained network can generate structured patterns for spatial light
modulation. Using the resulting structured patterns for spatial light
modulation, the feature information of target objects can be compressively
encoded into a short light intensity sequence. The resulting one-dimensional
signal is collected by a single-pixel detector and fed to the convolutional
neural network for objects classification. As experimentally demonstrated, the
proposed approach can achieve accurate and real-time classification of fast
moving objects. The proposed method has potential applications in the fields
where fast moving objects classification in real time and for long duration is
required.
