Abstract: A method of classifying an object according to an embodiment includes extracting a first feature by transforming rectangular coordinates of points included in the box of the object, obtained from a point cloud acquired using a LiDAR sensor, into complex coordinates and performing Fast Fourier Transform (FFT) on the complex coordinates, obtaining an average and a standard deviation as a second feature, the average and the standard deviation being parameters of a Gaussian model for the points included in the box of the object, and classifying the type of object based on at least one of the first feature or the second feature.

Abstract: An apparatus for classifying an object based on deep learning includes: a first deep learning device that performs deep learning for objects of a first class; a second deep learning device that performs deep learning for objects of a second class; and a controller that classifies objects on a road into the first class or the second class, classifies the objects classified into the first class for each type based on a learning result of the first deep learning device, and classifies objects classified into the second class based on a learning result of the second deep learning device.