Indoor Object Classification And Detection Based On Panoramic Image

Virtual reality has come into people's lives,and it can bring people immersive feelings.For the real estate industry,the traditional online search mode can only see the local housing information.I use of virtual reality technology to generate panoramic view of the house,users can view the 3D information,roam in the room through wearing VR devices and realize the intelligent interaction with the objects in the room.At present,panoramic image acquisition is complex,and the research of image detection and recognition mainly stays on the traditional image.In order to solve these problems,this paper designs a one key panoramic acquisition equipment,and realizes the detection and classification of indoor objects in the panoramic image.Through the deep research of the panorama stitching technology,this paper presents a design scheme of the whole system,and divides the whole system into three parts,which are image acquisition,image stitching and panoramic interaction.Three fisheye camera using multi thread synchronization technology to obtain the real image,after the fisheye correction and image registration,stitching,fusion process to achieve automatic panorama generation,build web server to achieve panoramic image display,save and share.The final completion of the entire system on the embedded platform,to achieve efficient access to the panoramic image,has the characteristics of low price,simple operation,easy portability.The actual product is put into use,also verified the stability and reliability of the system.About the classification and detection of objects in the panoramic image,taking into account the random deformation of the object in the panoramic image to bring a lot of difficulty to identify.In this paper,the very deep convolution neural network is used to extract features,and establish an object classification detection system based on region proposals.The detial is based on the panoramic image as input,deep convolutional neural network for feature extraction,and target region proposals generated by the candidate region of the network.Each region proposal adopts the feature mapping mechanism and the spatial Pyramid pool technology to get the normalized eigenvector.Then classification and detection network complete the object classification and bounding box regression.Finally,the object categories and positions are precisely exported by non maximum suppression algorithm.A large number of experimental results verify the feasibility and effectiveness of the classification detection system.The average accuracy of the indoor object detection is 68.7%.