Image processing method, device and electronic device including moving object

Abstract

The embodiment of the invention discloses an image processing method, device and electronic equipment including a moving object, and relates to the technical field of image processing. The method includes: extracting foreground feature points and background feature points of the current image by using depth information corresponding to the current image containing the operating target; determining a background motion vector based on the inertial data of the current image and the background feature points ; Obtain the current image pyramid of the current image and the historical frame image pyramid associated with the current image, and determine the foreground motion vector based on the current image pyramid, the historical frame image pyramid and the foreground feature points; The background motion vector and the foreground motion vector calculate the motion information of the moving object in the current image to form an object image associated with the current image. Through the foregoing solution, system resources can be saved, and image processing efficiency can be improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to the image processing technology including moving objects. Background technique [0002] Game rendering requires a lot of 3D model modeling, rasterization, texture mapping, rendering light and shadow effects, etc. The rendering complexity is very high, so it often causes frame rate bottlenecks. [0003] The prior art mainly aims at the complete video sequence, calculates the non-key frame motion vector off-line, and realizes the interpolation between frames. Such methods have poor real-time performance and complex algorithms, and have not been optimized for mobile platforms. In addition, the existing technology mainly uses color information to analyze images, and does not fuse depth data to reduce redundant calculations and improve efficiency; at the same time, it does not fuse depth data, and adopts multi-scale image processing methods for different depths. Finally, th...

AI Technical Summary

Problems solved by technology

Such methods have poor real-time performance and complex algorithms, and have not been optimized for mobile platforms

In addition, the existing technology mainly uses color information to analyze images, and does not fuse depth data to reduce redundant calculations and improve efficiency; at the same time, it does not fuse depth data, and uses multi-scale image processing methods for different depths

Finally, the existing technology mainly focuses on the accuracy and rapidity of optical flow calculation in color space, and does not propose the fusion method of depth data and the application in image interpolation

A large number of repetitive textures in color space and violation of optical flow assumptions will have a great impact on motion prediction

The existing technology is mainly aimed at the offline rendering of complete video data, or the online prediction of monocular video data, and does not involve the fusion of multi-objective and multi-dimensional information

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