A method for determining an object posture in an image, applied to a terminal device, wherein the terminal device is in communication with a server, and the method comprises: obtaining, by the terminal device, and before a real-time image is obtained at the terminal device, a training model parameter of a convolutional neural network for a target object from the server, wherein the server, in an offline training, obtains a standard image and a plurality of distorted images of the target object in a scene, and inputs the standard image and the plurality of distorted images into the convolutional neural network for training, wherein the convolutional neural network is trained by at least one image block from each of the plurality of distorted images as an input sample and at least one image block from the standard image as an ideal output sample, and the training model parameter includes an identifier of the target object that is preset on a client side of the terminal device and the server side; obtaining, by the terminal device, a real-time image of a determined target object, and identifying at least one first image block from the real-time image, the at least one first image block being a local image of the real-time image, wherein the determined target object corresponding to the real-time image is determined according to the obtained real-time image or current positioning information of the terminal device; determining, by the terminal device, through the convolutional neural network according to a determined training model parameter, a label image block matching each first image block, wherein the label image block is a local image of the standard image of the determined target object, wherein the determined training model parameter used for the matching when the determining of the label image block is performed is determined according to the identifier of the determined target object; and determining, by the terminal device, a posture of the determined target object according to each first image block and the label image block matching each first image block through affine transformation, wherein the affine transformation is indicated by an affine transformation matrix, the affine transformation matrix between each first image block and the label image block matching the first image block constitutes an affine transformation set, and the posture of the determined target object is determined by a matrix estimated value of the affine transformation set according to a least squares principle. obtaining, by the terminal device, and before a real-time image is obtained at the terminal device, a training model parameter of a convolutional neural network for a target object from the server, wherein the server, in an offline training, obtains a standard image and a plurality of distorted images of the target object in a scene, and inputs the standard image and the plurality of distorted images into the convolutional neural network for training, wherein the convolutional neural network is trained by at least one image block from each of the plurality of distorted images as an input sample and at least one image block from the standard image as an ideal output sample, and the training model parameter includes an identifier of the target object that is preset on a client side of the terminal device and the server side; obtaining, by the terminal device, a real-time image of a determined target object, and identifying at least one first image block from the real-time image, the at least one first image block being a local image of the real-time image, wherein the determined target object corresponding to the real-time image is determined according to the obtained real-time image or current positioning information of the terminal device; determining, by the terminal device, through the convolutional neural network according to a determined training model parameter, a label image block matching each first image block, wherein the label image block is a local image of the standard image of the determined target object, wherein the determined training model parameter used for the matching when the determining of the label image block is performed is determined according to the identifier of the determined target object; and determining, by the terminal device, a posture of the determined target object according to each first image block and the label image block matching each first image block through affine transformation, wherein the affine transformation is indicated by an affine transformation matrix, the affine transformation matrix between each first image block and the label image block matching the first image block constitutes an affine transformation set, and the posture of the determined target object is determined by a matrix estimated value of the affine transformation set according to a least squares principle.