flexible arm attached to one of the DISC unit. The thrusters ... Because the centroid calculation is based on intensity information, varying lighting conditions can ... circle and triangle markers were adjusted so that they had an area equal to the area of the medium .... designates the search region as a rectangle. Because the ...
AAS 03-592
AN INVESTIGATION OF VIDEO TRACKING ON A FORMATION FLYING SATELLITE TESTBED Erika Biediger* William E. Singhose* David Frakes** Munetaka Kashiwa *** Saburo Matunaga*** As the research for formation flying satellites moves from the theoretical arena to the experimental verification stage, video tracking during experiments becomes very important. The size, shape, and color of the marker play a significant role in determining how easily the satellite’s position, velocities and attitude are calculated both in real-time and offline. A satellite tracking algorithm was developed to accurately identify the locations of the satellites within the field of view. The image processing system uses colored markers located on the top of the satellites to identify the position. The objective of the work discussed here is to determine the best size, shape, and color for tracking markers that are used on experimental formation flying satellites. INTRODUCTION As the research for formation flying satellites moves from the theoretical arena to the experimental verification stage, video tracking during experiments becomes very important. The size, shape, and color of the marker play a significant role in determining how easily the satellite’s position, velocities and attitude are calculated both in real-time and offline. The formation flying testbed at the Tokyo Institute of Technology is an excellent place to test satellite tracking research. The ground experiment system shown in Figure 1 was constructed to verify the Robot Satellite Cluster Systems.1 The system consists of a 3m x 5m flat glass floor, three Dynamics and Intelligent control Simulators for satellite Clusters (DISC), and an image processing system measuring twodimensional position. The DISCs float on compressed air pads. Position and attitude are controlled by air thrusters whose commands are sent via a wireless local area network. The image processing system uses colored markers located on the top of the DISC units to identify the position. __________________________________ *Woodruff School of Mechanical Engineering, Georgia Institute of Technology **Biomedical Engineering, Georgia Institute of Technology ***Tokyo Institute of Technology Tokyo, JAPAN
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Figure 1: Dynamics and Intelligent Control Simulators.
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Time (sec) Figure 2: Flexible System Deflection. The objective of the work discussed here is to determine the best size, shape, and color for tracking markers that are used on experimental formation flying satellites. The end-goal for this research is to refine the tracking algorithm so that it is able to track multiple satellite bodies and their flexible appendages. The motion of the satellites is easier to track than the motion of their flexible appendages. The flexible appendages will oscillate at much higher frequencies than the satellite bodies. Figure 1 shows the deflection of one of these flexible appendages. Figure 2 shows the deflection of a flexible arm attached to one of the DISC unit. The thrusters were given a bang-bang command, and as seen in the figure, the endpoint of the flexible arm moves a significant amount. The fast motion of the flexible appendage increases the difficulty of tracking it using any tracking algorithm. The tracking code used here is an adaptation of an algorithm originally designed for tracking biomechanical motion, specifically that of pole-vaulters in action.2 This original
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algorithm was based on techniques used to accomplish motion estimation for frame prediction in camera video sequences. More recently, the methods that inspired this implementation have been adapted to perform in biomedical applications aimed at reconstructing magnetic resonance imaging data sets.3 The concept of motion estimation at the heart of these various algorithms relates well to the problem addressed by this paper. It is the motion of a specific target that we wish to capture. Target tracking in this work relies primarily on the intensity information that is provided by a video sequence. In addition to this raw information, geometric constraints are imposed based on a priori knowledge pertaining to the motion of the target to be tracked. First search regions are defined by the code operator in the initial frames of a video sequence to identify the areas in respective images where the moving target is found. Next, thresholding is applied to each frame in order to isolate a differentiable region of the moving object. All pixels within the selected image that occupy this region are used to determine the centroid of the target. Based on the first and second order derivatives of motion, the algorithm predicts the location of the target in the next frame and the search region for that frame is centered at the prediction. The thresholding operation is repeated for this next frame and the true location of the target is determined via the centroid calculation. Using this methodology an entire video sequence can be tracked automatically following the user input required for the first few frames. Because the centroid calculation is based on intensity information, varying lighting conditions can be problematic. A primary goal of this paper is to identify optimal tracking conditions, such circumstances should be implemented when possible. In the event that lumination cannot be controlled, a dynamic threshold value can be effective in maintaining accurate results. Since manipulation of this threshold is allowed, the tracking algorithm presented here is able to perform with greater accuracy than some more complicated implementations based on block-matching and optical flow. A simple experimental setup was constructed to move a system in a straight line and then determine which marker most accurately indicated straight line motion. STRAIGHT-LINE EXPERIMENTS Testbed Description Figure 3 shows the basic straight-line experimental setup. It consists of a track, marker background, and a marker. The colored marker is placed in approximately the center of the marker board. The marker board is then placed onto a cart that is constrained to move in a straight line by the track. The cart is then pulled along the track while an overhead camera records the movement. The overhead camera records the movement of the marker onto a VHS cassette. The track can be placed at any orientation relative to the camera field of view. Each of the experiments was digitized into sequence of bitmaps. The tracking algorithm was then applied to the sequence of bitmaps to identify the position of the marker.
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Figure 3: Straight-Line Satellite Experimental Setup. Table 1: Straight Line Experimental Variations. Marker Colors Marker Shapes Marker Marker Sizes Background Colors White Red Circle Small Black Blue Square Medium White Triangle Large Square
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Table 2: Straight-Line Experimental Parameters. Description Size Marker Background 450mm x 380mm Track length 1m Small Marker Size Sides = 6.25 cm Radius = 6.25 Medium Marker Size Sides = 12.5 cm Radius = 12.5 cm Large Marker Size Area of marker = 156.25 cm2 Table 1 shows the various combinations of marker shape and color tested. Two marker backgrounds were used: white and black. Three marker shapes where utilized: circle, square, and triangle and four marker colors were used: red, blue, black, and white. Table 2 shows the dimensions of the different marker sizes, track and marker background. Three different sizes were used for the markers: small, medium and large. For the large size, only the circle and triangle marker shapers were tested. The large size circle and triangle markers were adjusted so that they had an area equal to the area of the medium square marker. The large marker size experiments were completed in order to determine if tracking quality was a function of basic shape rather than area. Three orientations for the track were used: 0°, 30°, and 60°.
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Figure 4: Straight-Line Satellite Experimental Results. There are many different factors that limit the effectiveness of the tracking algorithm. It is possible for the tracking algorithm to lose the location of the marker. If the color of the general background and marker do not have a large enough color contrast, it may be impossible for the algorithm to find the marker. For example, a black general background may be unsuitable for use with a black marker background, as it maybe difficult for the algorithm to distinguish between the general background and the marker background. Scaling the black and white threshold value may increase the grayscale difference between the two backgrounds. However, the best combination of marker background and general background is one that gives the greatest color difference. Another area of difficulty arises due to the overhead lights. If the overhead lights are too bright, the color intensity of the marker changes as it passes beneath the lights. If the change in intensity is severe enough, the algorithm will be unable to distinguish where the marker is. Another potential problem can occur due to the reflection of the bright overhead lights off of the glass floor. If the reflection is too bright, the contrast between the general background and a light colored background the algorithm will be unable to locate the marker. The experimental results are discussed in the following sections. Experimental Results Because the algorithm had difficulty tracking any of the markers when a dark marker background was used, all of the presented results are for the white marker background. Figure 4 shows the results of the tracking algorithm for a 0°, 30°, and 60° experiment. As seen in the figure, the algorithm shows that the centroid of the marker travels along a fairly smooth straight line. In order to avoid any distortion from the overhead camera’s lens effect, the track was placed as close to the center of the workspace as possible. Figure 5 demonstrates the excellent consistency of results from the tracking algorithm. The figure shows three runs of the same experimental data – the
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Figure 5: Repeatability of Tracking Algorithm.
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Figure 6: Medium Size Triangle 0º Experiments. medium sized red marker circle data. The Residual Y Position shown on he y-axis represents the difference between the experimental data and a best fit line through the data. As seen in the figure, there is less than a 1mm difference between the three runs. This shows that the algorithm can provide consistent results for repeated experiments. Although the experiment data shown in Figure 4 appears to be relatively smooth, upon closer inspection it is revealed that the tracking algorithm’s calculated position is not as smooth. Figure 6 shows the results for the medium sized triangle experiments for the black and red markers. As seen in the figure, the general path of the marker is still a relatively straight line; however, the tracking algorithm’s calculated position of the centroid varies up to 3mm.
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Figure 8: Blue Marker Equal Area 30º Experiments. In order to test whether the precision of the tracking algorithm was independent of marker size, the equal area experiments were conducted. Figure 7 shows the results for the equal area black marker 0º experiments. As seen in the figure, the calculated position of the centroid travels in a relatively straight line, and there is little difference between the three different shapes. Figure 8 shows the blue marker equal area 30 ° experiments. The tracking algorithm deviates from the best fit line through the data significantly more for θ = 30 ° experiment when compared to the θ = 0 ° experiment. Figure 9 shows the blue marker equal area 60 ° experiments. As θ increases, the tracking algorithm has an increased difficulty in tracking some of the shapes. The tracking algorithm does not track the triangular shape as well as the circular or square shape. This is because when the
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Table 3: Straight-Line θ=0º Statistical Results. Marker Size Marker Color RMS Medium Square Medium Square Medium Square Medium Triangle Medium Triangle Medium Triangle Large Circle Large Circle Large Circle Large Triangle Large Triangle Large Triangle
Black Blue Red Black Blue Red Black Blue Red Black Blue Red
0.53599 0.26188 0.85659 0.65649 1.2207 0.5717 0.36258 0.37951 0.59118 0.45075 0.60684 0.55836
tracking algorithm first asks the user to designates the search region as a rectangle. with respect to this search region, it is to leave the search region and distort the is important to note that the difference 60 ° data and the calculated position is less reasonable job of tracking even the
Standard Deviation 0.53773 0.26317 0.86003 0.65882 0.70446 0.57433 0.36355 0.38199 0.59401 0.45231 0.60989 0.56055
specify the centroid of the marker, it Because the triangle is now at an angle possible for parts of the triangular marker calculated centroidal position. However, it between the best fit curve line for the θ = than 10mm, so the algorithm does an triangular shape.
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Table 4: Straight-Line θ=30º Statistical Results. Marker Color RMS
Marker Size
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Medium Square Medium Square Medium Square Large Circle Large Circle Large Circle Large Triangle Large Triangle Large Triangle
Black Blue Red Black Blue Red Black Blue Red
2.8511 2.3146 2.0935 1.1071 0.84140 1.2318 1.5222 1.6915 1.5042
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Standard Deviation 2.8701 2.3298 2.1072 1.1102 0.84605 1.2377 1.5268 1.696 1.5073
Red Large Circle Blue Large Circle Black Large Circle
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Statistical Analysis Although the graphical representations of the experiments is helpful, it is difficult to determine which marker size, shape and color is the best combination to utilize with the tracking algorithm. In order to differentiate between the experimental results, statistical analysis was performed on the data. Table 3 shows the standard deviation and the RMS (root mean square) error for some of the θ = 0° experiments. In general, the blue marker provided the best overall performance for the different shapes. The experiment with the lowest standard deviation was the blue square marker. The large black circle also had good performance. Overall, the tracking algorithm showed good success in tracking the circular shaped markers. The statistical results for the θ = 30° experiments are shown in Table 4. The circular shaped marker has the lowest error for all three different colors. Figure 10 shows
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Medium Square Medium Square Medium Square Large Circle Large Circle Large Circle Large Triangle Large Triangle Large Triangle
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Table 5: Straight-Line θ=60º Statistical Results. Marker Color RMS Black Blue Red Black Blue Red Black Blue Red
2.8511 2.3147 2.0887 3.5691 2.4094 2.8011 3.3485 2.0932 2.0646
Standard Deviation 2.8701 2.3299 2.1022 3.5948 2.4256 2.8208 3.3741 2.1086 2.0788
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Figure 11: Medium Size Square 60º Experiments. the results for the different colored red markers. As seen in the figure, the overall tracking is excellent especially for the blue colored marker. The statistical results for the θ = 60° experiments are shown in Table 5. For this particular set of experiments, the medium square marker (in all colors) and the large circular marker (in blue and black) significantly outperformed the triangular shaped marker. As the angle increases, the chance increases for more of the triangle marker to leave the predefined search region. Figure 11 shows the θ = 60° residual y position for the medium sized square marker. CONCLUSION In general, the tracking algorithm presented here will be able to calculate the position of the satellite and its flexible appendage with high accuracy. Straight-line
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experiments were conducted to determine what kind of effect the marker color, shape and size has on the tracking algorithm. The tracking algorithm had difficulty tracking any of the markers when using a black marker background because the general background was very dark. The algorithm had excellent success tracking all of the markers when using a white background. The tracking algorithm had excellent performance when tracking all of the markers with the highest deviation of less than 10mm. For the θ = 0 ° experiments, the tracking algorithm had good success tracking all of the markers. However, when the angle was increased to 30°, and 60°, the algorithm showed better results when using the square and circular markers. In general, the algorithm had better success in tracking the blue markers when compared to the red and black markers. ACKNOWLEDGEMENT This research was funded in part by NASA Goddard GSRP Fellowship and an NSF Ph.D. Dissertation Enhancement grant. REFERENCES 1. 2.
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Matunaga, S., et al. Robot Satellite Cluster System and its Ground Experiment System. in 51st International Astronautical Congress. 2000. Rio de Janeiro, Brazil. Frakes, D. and W. Singhose. Geometrically Constrained Optical Flow-Based Tracking for the Extraction of Kinematic Data from Video. in Fifth International Symposium on Computer Methods in Biomechanics and Biomedical Engineering. 2001. Rome, Italy. Frakes, D., et al. Application of an Adaptive Control Grid Interpolation Technique to Morphological Vascular Reconstruction. in IEEE Transactions on Biomedical Engineering. 2003.
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