A video-based theodolite simulation system

Transcription

1 Advanced Materials Research Online: ISSN: , Vols , pp doi: / Trans Tech Publications, Switzerland A video-based theodolite simulation system Zhong Jian 1, a, Zheng Zhe b 1 Beijing University of Aeronautics and Astronautics a @qq.com, b Zheng Zhe@sian.com Keywords: Simulation; Panorama; SIFT; HOG; Theodolite Abstract. Realized a Video-based theodolite simulation system, simulating and modeling theodolite servo control system with Simulink, build the model of theodolite movement. Split the recording video into images, and projected it onto the virtual coordinate system, then stitching images by SIFT method, the result panorama is used as background. Designed a algorithm based on gradient histogram, detect the target from the video image, then superimposed it on the background. System achieve a high degree of simulation, and reproduced the mandate process of the theodolite. Introduction At present, the photoelectric theodolite is still the higest precision measurement system in spatial dynamic target location, it has many advantages such like real-time, high accuracy, dynamic tracking and image reproduction and more, so it is widely used in scientific research and military fields. The theodolite system is huge and complicated, so it is difficult to simulate the theodolite. Therefore, the implementations of the theodolite simulation are basically in the theodolite s sub system, and most of them using graphics method. For example, simulate the theodolite control and hardware or simulate the theodolite image. In this paper, we simulate racking screen of theodolite. By establishing the mathematical model of theodolite control servo, we can use the input of single rod to compute the motion parameters. We use theodolite to scan the bacgrand (always sk, and use SIFT algorithm to stitch images. The result panorama is the background of simulation. Then break down the real video into BMP image sequences, using the HOG algorithm to detect and separate the target from the image, and record the time and location information. Finally, integrate the target and the pre-acquisition background to get the panoramic pictures. The automatically video based generating of Panorama First, theodolite collected the background panorama before task. Example, if the FOV is one degree, taking one picture once per rotation beginning of the azimuth zero. So that we can get 360 * 90 pictures. Stitching every two adjacent pictures with SIFT method, and then cutting the results according to the original boundary, in fact the background panorama shooting stitching is a cylindrical panorama. A. Scale-invariant feature transform SIFT(Scale-invariant feature transform)algorithm is used to detect and describe the localized features in image. It looking for extreme points in spatial scales, and extract its position, scale and rotation invariant, It is a feature-based registration method. Each feature point information is(x,y,α, β,m,w),in these parameters,(x, Y)is location, α is Zoom degrees, β is he main direction,m is response strength,and the parameter W( W, W, W ) is 128-dimensional descriptor W128 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (ID: , Pennsylvania State University, University Park, USA-13/05/16,11:54:59)

2 Advanced Materials Research Vols P ( i 1, j P ( i 1, P ( i 1, j+ A B P ( i, j P ( i, P ( i, j+ 4 D O C 5 6 P ( i + 1, j P ( i+ 1, P ( i + 1, j Figure 1 Image array Figure 2 Image block Matching feature points used to establish the corresponding relationship between the feature points of two images.matching of feature point matching is the comparison of sub-error energy feature points to descriptor. The Similarity measure between two descriptors 128 i ) = ( rij sij ) d R S The matching of key points generally uses a data structure called K-D tree to complete the search. The critical points of the target image is the origin, search the feature points of original image which points are closest to the target image feature points and the next near points.. B. Panorama synthesis and display Split the fitting picture according to the original boundary, I.e. from the original image sequence P, We get new sequence of images that have been fitted P By Searching and Comparing the theodolite angle (A, E) and the angle information of the image in index table, we can quickly find the image in which the center of the optical axis located, as if the image isp By the formula, the coordinates of optical center O on the image P can be calculated. j= 1 Call in the eight images that adjacent and Diagonally adjacent with thep, and rebuilt the coordinate system Calculate the coordinates of four points ABCD, Interception rectangular area which formed by ABCD and displayed directly as a background Create a plane rectangular coordinate system OXY optical center of the visual axis through the origin O (0,0) and perpendicular to the plane on the CCD imaging array. The coordinates of the target on the CCD imaging angle a one-to-one correspondence with the target and the optical center by P (x, can be obtained A and E: A =arctg( x / f ) E =arctg( y / f ) (2) We also can use the theodolite angle value to get the coordinates on the image P(x,. The image pixels is Height Width. Since images have been stitched and split according to the original boundary, so the rectangular area ABCD can be used directly. Extraction and integration of the target 2 First we decomposition the video of task and give image sequence, then extracte target from the image and projected it onto the panorama background in accordance with its coordinates. At present there are many methods of target detection and extraction,such as Color histogram, Phase correlation and optical flow. Theodolite image has simple background, and obvious target contour. In this paper, we use HOG feature to extract the target, The HOG feature can reflect the (

3 274 Advances in Applied Science and Industrial Technology distribution of the gradient strength and gradient direction of the local area of image, describe the local object appearance and shape features. A Object extraction based on HOG The algorithm uses the local features of the image as the feature vector of the entire target object. The gradient of the image is calculated to obtain the information of the image edge. Image preprocessing: Noise generated by the image formation or transfer process, can be reduced by Gaussian filtering method. Gradient calculation: In the gradient direction histogram algorithm, we use one stepped Gradient of image for target recognition. The derivation process can capture the contours, edge and gray texture information of the object, and further weaken the impact of light. The acquisition of the local feature: The algorithm segment image into image slice, each image slice has height width pixels, called "cell". Such cells may be rectangular region or annular region. It is also localized areas of the image. The gradient determined by the direction angle and amplitude. In order to integrate the local characteristics, the algorithm projected the value of the gradient of each pixel on to a fixed number of orientations, using Bilinear or linear interpolation. Normalization process: Normalized image, looking for a set of parameters through the use of the image moment invariants in order to eliminate other transformations function on the image transform. It s converted into the standard form only in order to resist the impact of the affine transformation or non-uniform illumination. Because of the localized illumination, if only using one cell for the localized feature extraction, the noise may spread in the local area. The algorithm uses the cell reorganization excluding this effect. Calculating the locality features in a larger area. Such as the method of combination of blocks, we integrated several cells into a block. Make sure there are overlap portion between the blocks. Due to the presence of overlapping cell, the cell will be calculated in different blocks, So that the cell or local area information can be reflected in different blocks, makes the impact of locality sudden change smaller. B Algorithm realize Enter the sample image Using gradient feature operator, To strike the Color differenceg x, G y which has dramatic changes in direction X or direction Y, and strike a gradient amplitude G and direction angle O G 2 2 y ( x, of each pixel point. G( x, = Gx ( x, + Gy ( x, a( x, = arctan( ) (3) Gx( x, Defining a predetermined value of the angle in each direction. Project direction angle O and gradient amplitude G of each pixel using linear interpolation method. Divide the image into cell, calculated histogram based on the direction in each cell. Integrate the feature vector of the cells that in one image block into a one-dimensional vector, process data using the normalized function. Make sure cell overlap exists between the image block and image block. Integration of the image block vector, sort vectors in the feature vector interval of the entire sample image. Experiment and evaluate As an experiment, we follow the actual tracking video for the reproduction task, and theodolite interpretation data as the true value, through the use encoder and video storage computer record all the simulation data(include angle data, simulation video ). A Deviation of simulation system By comparing with the true value, the difference between the virtual encoder value and the theoretical value can obtained per one sampling interrupt. Azimuth(red),site(green)Curve shown in Figure

4 Advanced Materials Research Vols Figure 3 Deviation of simulation system Calculate the mean square with error of the azimuth and site.δa= ,δE= ,the maximum angle measurement error less than 1 (not exclude the causes of communication delay and accuracy of the model). B automatically target extraction In the experiment, we examined the automatically target extraction algorithm. Because it is a preparatory work, so we do not simplified it for acceleration, but tend to add its accuracy. From statistical data, we obtained a 96.2% precision and recall detection efficiency of 90%. Respect to the traditional methods, our method has significantly improved performance. But in some conditions, for example, multi-objective and background contour stronger than the target, algorithm will get wrong result. Next step should add the function of the human-computer interaction to deal this. REFERENCES [1] Lowe DG, Distinctive image feature from scale invariant key point [j]. International Journal Computer Vision, 2004,60(2), pp [2] Xiaoyu Wang, Tony X. Han and Shuicheng Yan. An HOG-LBP human detector with partial occlusion handling. In CVPR 2009, pp [3] Li YanLi,Xiang Hui, Robust and Automatic Spherrical Panorama Stitching,Journal of Computer-Aided Design&Computer Graphics,Vol.19no.11 pp ,2007 [4] N. Dalal and B. Triggs: Histograms of oriented gradients for human detection. In CVPR 2005, volume 1, pp , [5] Xiaoyu Wang, Tony X. Han and Shuicheng Yan. An HOG-LBP human detector with partial occlusion handling. In CVPR 2009, pages [6] GONG Jing-long DENG BIN, Manual following training system of photoelectric theodolite Electronics Optics&Control Vol.13,No.04,2006 [7] Chao Zhu, Charles-Edmond Bichot, Liming Chen. Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition. International Conference on Pattern Recognition (ICPR), IEEE ed. Istanbul, Turkey. pp , [8] Yu Zheng Lin,TAN WEI,JIAN Tao, Training simulator for photoelectric theodolite based on visual emulation, In Journal of Jilin University 2012 Volume 41 No.2, pages [9] Shuai Zhang, Xiang Chen, Kongqiao Wang, Jiangwei Li, Yanwei Pang, He Yan. Active Histogram of Oriented Gradient Baesd Learning for Palm Tracking. Advances in Intelligent and Soft Computing,2012,Volume 133/2012, ,DOI: / _91.

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