THE POTENTIAL OF OBJECT RECOGNITION USING A SERVO-TACHEOMETER TCA2003

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1 2 Technical Session THE POTENTIAL OF OBJECT RECOGNITION USING A SERVO-TACHEOMETER TCA2003 Dipl.-Ing. Peter Wasmeier Lehrstuhl für Geodäsie Technische Universität München p.wasmeier@bv.tum.de KEY WORDS: object recognition, video tacheometer, automation, image processing ABSTRACT Modern tacheometer realize the requirement of target recognition by means of an integrated infraredsensitive CCD-camera chip. Using this, also images of real objects can be made and their contents be evaluated with image processing methods. Together with the possibility of high precision angular measurement an automatic targeting on non-signalled points is possible. Using largely off-the-shelf tacheometers however, brings up some limitations. Demonstrated by an exemplary algorithm to detect steeples the existing potential of such instruments for tasks of object recognition shall be shown. 1 INTRODUCTION Motorized target tracking stations have become omnipresent in geodetic practice about 15 years ago. They work independent of an observer, remote or software controlled, on reflectors or other targets and are useful wherever staff costs have to be reduced, long-lasting monitoring tasks have to be performed or continuous online data gathering and evaluation is necessary. By now, all leading manufacturers of surveying instruments serve the upper market segment with such instruments. The ability of automatic target recognition (ATR) and tracking is realized by an integrated CCD-chip. The reflection of an active infrared beam by the target is mapped on the chip, so that its position in reference to the reticule can be calculated by means of simple gray-value-operation tasks. The achievable accuracy is a few tenths of pixels and therefore the positioning of the field of view on the target can be performed with about 0.2 mgon (Bayer, 1997). Standard tracking con be done very quick and robust, but the possibilities offered by an integrated camera mounted on a high precision angle measurement device are not exploited. This paper shows the results of testing an usual-in-trade total station Leica TCA2003 for automatic analyzing of real-light scenes. To obtain general suitability and restrictions of the given instrument, a fusion of classic surveying and image analysis methods was necessary. For evaluating the efficiency and accuracy, an exemplary algorithm was developed to automatically recognize steeples and point the collimation axis of the instrument towards the target point (knob or cross). The process of developing such an algorithm will be presented in detail. The work this paper is based on was done as a diploma thesis at the chair of geodesy, TU München.

2 The Potential of Object Recognition Using a Servo-Tacheometer TCA LIMITATIONS ARISING FROM THE INSTRUMENTS Difficulties mainly come from the tacheometer itself, which is not designed for image processing tasks. Some constructional points restrict the possible performance: The TCA2003 has to be retrofitted with a video signal output. This must be done by the manufacturer. Incoming radiation is split and filtered in the optical path, allowing the infrared part to pass towards the CCD-chip in preference. This is desirable for prism detection, but makes processing of natural scenes rather hard. Because of the infrared sensitivity background illumination resulting from insolation reduces contrast quality. The beam-splitting cube is positioned in front of the focus lens in the optical path. Therefore camera-taken images cannot be focussed on an object. This is only of little interest for spotting prism reflection by their mass center, but makes real-light picture quality very poor. Objects not having a minimum distance of about 400 meters to the tacheometer can hardly be evaluated. Image processing can be very complex. Therefore an external computer with proper framegrabber hardware and capable image-processing software tools is necessary. This computer also performs the tacheometer control, so various cable connections have to be set up. Regarding these problems, automatic object recognition tasks either have to be chosen accordingly or further construction changes have to be made. 3 OBJECTS OUT OF GRAY VALUES To extract an object (or class of objects) from a real scene picture, a modelling process has to be performed before. In this step, unchanging characteristics and features of the real objects appearance must be formally described and linked to the unique attributes of the mapped objects. This has to be done considering the radiometric and geometric information, semantic modelling then builds up on both. symmetry vertical beam horizontal beam sphere ellipsoid cross and/or church knob pole disturbing objects weather vane decorations roof Figure 1: The object model for a church steeple For the steeple example, a simple model consisting of a roof, a cylindrical pole, cross bars and/or an elliptic knob and possible disturbing objects is sufficient. Knowledge of the symmetry in respect of the vertical tower axis is fundamental, because this offers a sensible evaluation strategy. The theoretical object model can be seen in figure 1.

3 4 Technical Session The predefined model then is tried to recover in the tacheometer generated picture using a knowledgebased image analysis. Starting with an edge detector and together with the radiometric information of the edge-enclosed regions, weighted assumptions for the modelled steeple parts, background and various disturbances can be created. These become verified regarding relative distances, directions of edges and symmetric behavior; those fitting best are the most likely results then. To speed up evaluation, some pre-processing steps can be performed which reduce the region of interest (ROI), control the contrast or eliminate blooming areas (i.e. sun reflections on roof parts result in bright white areas which seem to expand on the background - compare fig. 5) which lead to interfering edges. Using the known rotation parameters of the camera chip s orientation, the image also can become levelled. It then is parallel to the standing axis of the total station, and information about symmetry found in the image can be meaningful connected to the real object. Figure 2: Calculated center pixels for the symmetry axis and the finally found axis Because it is known from the modelling process, that the steeple is nearly symmetric related to its vertical middle axis, this is searched for in the image taken. An algorithm checks all possible pairs of edge pixels, line by line, for their direction and calculates the corresponding center pixel if needed. From the resulting weighted amount of center pixels (fig.2 left) a best fit line is calculated (fig.2 right). This algorithm has proved to be very robust in tests with various church towers. To assign the detected edges to modelled object parts, they have to be approximated as simple geometric primitives like line and arc segments. A simple, iterative polygon approximation of edges was introduced by (Ramer, 1972).The found polygons are split up to archive straight lines, and it is tried to connect neighboring segments with arcs. The lines are grouped to parallel pairs and build the cross bar assumptions; the arc parts are used to calculate knob ellipses assumptions using a least squares adjustment. The final target point - either the lower border of the church knob or the transition from the steeple roof to the cross - is then ascertained from the various assumptions regarding their weighting and further position restrictions. Examples for different church towers are shown in fig.3. The target pixel coordinates can be determined with an accuracy of a few tenths of pixels, which is comparable to the TCA2003 s angle measurement accuracy of 0.15 mgon.

4 The Potential of Object Recognition Using a Servo-Tacheometer TCA Figure 3: Examples of detected steeple parts and the resulting target points 4 CALCULATING ANGLES After the target point is spotted, the theodolite telescope can be repositioned on it. Therefore the angular differences of the target from the reticular point are calculated from the pixel difference in the image using an affine transformation dhz = a 1 (R R 0 ) + a 2 (C C 0 ) (1) dv = b 1 (R R 0 ) + b 2 (C C 0 ) (2) as shown by (Bayer, 1997). R 0 and C 0 describe the reproduction point of the reticule in the image; the parameters a 1 to b 2 imply scale changes when switching from pixels to angular values, and parameters for the chip rotation and for the relative chip axis bearing (cp. (Schirmer, 1994)). They are determined by the manufacturer to perform ATR tasks, and must be adopted to suit the grabbed pixel size when evaluating images from a framegrabber. There are a few sources of error which limit the accuracy of the determined reposition angle differences: The affine parameters given by the manufacturer often have little post-comma precision. 50 pixels away from the center we already get an uncertainty of 0.5 mgon, so a two-step repositioning with coarse and fine adjustment has to be performed. The horizontal angular value is not independent of the actual vertical angle and has to be projected in the horizontal plane first. This effect can produce errors of 50 mgon and more. For the vertical angular measurement no further corrections are necessary. Common other error influences, like slanted axes, can be eliminated by measuring in both observing positions like it is done when observing manually.

5 6 Technical Session Once the final angular corrections are computed, the telescope is repositioned automatically and the extracted object is stored in memory together with its edge information and bearing to enable easy retrieval. The detection of the target points is the most time consuming part in the evaluation chain. For repeated measurements (e.g. during a set measurement) it is replaced by an edge-based matching algorithm: the lens is moved to point towards the stored direction and the actual taken picture only becomes matched with the edge information of the object in memory. This yields a more robust and even faster detection which in addition performs completely automatically (Steger, 2000). In addition, even targets which couldn t have been detected by the automatic algorithm before (e.g. because of insufficient contrast) can be added to the measurement set. The accuracy of object extraction and matching algorithm is about 0.5 mgon for both when using good quality images and about 2 mgon when using very poor quality. These deviations can be traced to air flickering, image noise and insufficient contrast and can be reduced stochastically by repeated measurement. 5 PRACTICAL TEST: AUTOMATIC RESECTION Figure 4: The instrument configuration on the surveying roof of the TU München To perform a practical test a resection problem with three steeples was set up an the surveying roof of the TU München (fig.4). Therefore all the steps mentioned above have been implemented in a computer program to control the process. During a teaching phase the telescope is roughly pointed towards the targets, and the algorithm automatically extracts the desired points. These become verified by an operator, repositioned and stored in memory. By now, this step only works semi-automatic, because picture quality sometimes is not sufficient due to the constructional limitations mentioned in section 2. After this starting phase, the set measurement is performed using the matching algorithm and works completely automatically. For time and accuracy comparison, a three set measurement was performed with the computer system first, and then controlled by an human operator. As having good weather conditions, the targets were found without any problems.

6 The Potential of Object Recognition Using a Servo-Tacheometer TCA Figure 5: Insolation on a church tower during the course of a day While the teaching phase takes rather long time (approx. 1 minute per target), the measurement itself works quicker than a human operator could do. Altogether, for collecting three sets both systems need approximately the same time; with increasing number of sets the computer-driven system succeeds. The absolute measurement results match up in the range of less than 1 mgon (including all environmental influences). The standard deviations of both measurement methods are nearly identical in a range of 2-5 mgon/10, and the repetitive accuracy of computer driven single measurements is about 2-4 mgon/10. This is similar to an experienced and thorough human operator and shows that the automatic extraction and positioning system can be compared to conventional surveying techniques. Furthermore, it showed to be less influenced by reflection effects on metallic roofs when sun is moving in the course of day as shown in fig.5 (humans tend to point to slightly shifted target points when strong insolation appears) and - of course - a computer never fatigues. 6 CONCLUSION The built-up automatic system has fulfilled the desired tasks comparable to a human operator. Nevertheless, it strongly depends on the quality of the modelling and its implementation. The main restricting factor is poor image quality arising from the tacheometer construction. Also, many extra components are necessary at the current state which reduce fieldwork suitability. It has been showed, that image processing tasks in principle can be performed on tacheometergathered pictures and used even for surveying tasks of higher accuracy demands. Automatic target recognition of non-signalled objects therefore holds a rather big, but yet hardly used potential. This is the fact especially because of the little number of mass-market applications, which in addition do not encourage the manufacturers to build instruments more suitable for optical processing tasks. One current exception is the Sokkia tacheometer SET3110MV study, which was introduced at the Intergeo fair 2002 and enables focussed color-images. At present time, this only is a prototype - but it could show up the next generation in tacheometer surveying. Moreover, most of the possible applications demand a high degree of single case preparation - especially because of the individual modelling work and time schedules which have to be made. Those are both time-consuming and expensive. Nevertheless, the combination high precision geodetic instrument and artificial eye with image processing evaluation strategies will be a market segment in the future. Standard tasks can easily be performed with lots of instruments; but for individual problems often tailored solutions and continuing measurement concepts are necessary. One solution is the information, digital tacheometer images hold. REFERENCES Bayer, G., Dynmaic aspects of the tca1800 automatic total station. In: Proceedings of the Optical 3D Measurement Techniques IV Congress in Zürich, Wichmann Verlag, Karlsruhe. Leica Geosystems AG, Persönliche Mitteilungen.

7 8 Technical Session Ramer, U., An iterative procedure for the polygonal approximation of plane curves. Computer Graphics and Image Processing pp Schirmer, W., Universaltheodolit und CCD-Kamera - ein unpersönliches Messsystem für astronomisch-geodätische Beobachtungen. PhD thesis, Fakultät für Bauingenieur- und Vermessungswesen an der TU München, DGK-Veröffentlichung Reihe C Band 427. Steger, C., Similarity measures for occlusion, clutter and illumination invaraint object recognition. In: Pattern recognition, Proceedings of the 23rd DAGM Symposium in Munich, Springer Verlag. Wasmeier, P., Potential der Objekterkennung mit dem Videotheodolit TCA Master s thesis, Lehrstuhl für Geodäsie und Lehrstuhl für Photogrammetrie und Fernerkundung an der TU München.