461 lines
20 KiB
Plaintext
461 lines
20 KiB
Plaintext
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ARMY RESEARCHLABORATORY
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Scale-Insensitive Detection Algorithm for FLIR Imagery
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Sandor Der, Chris Dwan, Alex Chan, Heesung Kwon, and Nasser Nasrabadi
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AKL-TN-175
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February 2001
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Approved for public release: distribution unlimited.
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2001032064
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The findings in this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.
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Citation of manufacturer’s or trade names does not constitute an official endorsement or approval of the use thereof.
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Destroy this report when it is no longer needed. Do not return it to the originator.
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Army Research Laboratory
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Adelphi, MD 20783-I 197
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ARL-TN- 175
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February 2001
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Scale-Insensitive Detection
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Algorithm for FLIR Imagery
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Sandor Der, Chris Dwan, Alex Chan, Heesung Kwon, and Nasser Nasrabadi
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Sensors and Electron Devices Directorate
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Approved for public release: distribution unlimited.
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Abstract
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This report describes an algorithm for detecting military vehicles in FLIR imagery that will be used as a prescreenerto eliminate large areasof the image from further analysis. The output is a list of likely target locations with confidence numbers to be sent to a more complex clutter-rejection algorithm for analysis. The algorithm usessimple featuresand is intended to be applicable to a wide variety of target-sensor geometries, sensor configurations, and applications.
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ii
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Contents Fiigures
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1 Introduction
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1
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2 Data
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2
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3 Features
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3
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3.1 Maximum Grey Level, Feature 0 . . . . . . . . . . . . . . . . 3
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3.2 Contrastbox, Feature 1 . . . . . . . . . . . . . . . . . . . . . . 3
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3.3 Average Gradient Strength, Feature 2 . . . . . . . . . . . . . . 4
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3.4 Local Variation, Feature 3 . . . . . . . .............
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4
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3.5 How FeaturesWere Selected . . . . .............
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4
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4 Combining Features
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6
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5 Experimental Results
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7
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6 Conclusions and Future Work
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13
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References
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14
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Distribution
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15
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Report Documentation Page
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1 ROC curve on Hunter Liggett April 1992imagery ........
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8
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2 ROC curve on Yuma July 1992imagery ..............
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8
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3 ROC curve on Greyling August 1992imagery ...........
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9
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4 Easy image from Hunter Liggett April 1992dataset .......
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9
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5 Results on image in figure 4 .....................
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6 Moderate image from Hunter Liggett April 1992dataset ....
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7 Results on image in figure 6 . . . . . . . . . . . . . . . . . . . . . 12
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. . .
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111
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1. Introduction
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We designed the algorithm described in this report to addressthe need for a detection algorithm that could serve asa prescreener/detector for a broad number of applications. While most automatic target detection/recognition (ATD/R) algorithms usemuch problem-specific knowledge to improve performance, the result is an algorithm that is tailored to specific target types and poses.The approximate range to target is often required, with varying amounts of tolerance. For example, in some scenarios,it is assumedthat the range is known to within one meter from a laser range finder or a digital map. In other scenarios,only the range to the centerof the field of view and the depression angle is known, so that a flat-earth approximation provides the best estimate. Many algorithms, both model-based and learning-based, required either accurate range information or compensate for inaccurate information by attempting to detect targets at a number of different ranges within the tolerance of the range. Becausemany such algorithms are quite sensitive to scale, even a modest range tolerance requires that the algorithm attempt to match at a large number of closely spaced scales,driving up both the computational complexity and the false alarm rate.Algorithms have often used view-based neural networks [l- 31or statistical methods [41.
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The proximate motivation for developing the scale-insensitive algorithm was to provide a fast prescreener for a robotic application for which no range information was available. Instead, the algorithm attempted to find targets at all ranges between some reasonableminimum, determined from operational requirements, and the maximum effective range of the sensor.
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Another motivation was to develop an algorithm that could be applied to a wide variety of image sets and sensor types without the severedegradation in performance that commonly occurs with learning algorithms, such as neural networks and principal component analysis-basedmethods, that have been trained on a limited variety of sensor types, terrain types, and environmental conditions. While we recognize that with a suitable training set, learning algorithms will often perform better than other methods, such a scenario typically requires a large and expensive training set, which is sometimes not feasible.
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2. Data
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The dataset used in training and testing this system was the April 1992 Comanche forward looking infrared (FLIR) collection at Ft. Hunter Liggett, CA. This datasetconsists of 1225images, eachof which is 720by 480pixels. Each image has a field of view of approximately 1.75degreessquared.
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Each image contains one or two targets in a hilly wooded background. Ground truth was available that provided target centroid, range to target, target type, target aspect, range to center of field of view, and depression angle. The target centroid and range to target were used to score the algorithm, as described in the experimental results section, but none of the target-specific information was used in the testing process.The algorithm assumes that only the vertical and horizontal fields of view and the pixel geometry are known. The only range information used is the operational minimum range and the maximum effective range of the sensor.
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3. Features
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Each feature is calculated for every pixel in the image. As more complex features are added in the future, it might become beneficial to calculate some of the features only at those locations for which the other feature values are high. While eachfeature assumesknowledge of the range to determine approximate target size, these featuresare not highly range sensitive. The algorithm calculates each feature at coarsely sampled ranges between the minimum and maximum allowed range.
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Each feature described below was chosen based on intuition, with the criteria that they be monotonic and computationally simple. The features are described in decreasingorder of importance.
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3.1 Maximum
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Grey Level, Feature 0
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The maximum grey level is the highest grey level within a roughly targetsized rectangle centered on the pixel. We chose it becausein many FLIR images of vehicles, a few pixels are significantly hotter than the rest of the target or the background. These pixels are usually on the engine, the exhaust manifold, or the exhaust pipe. The feature is calculated as
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where f(k. 1)is the grey-level value of the pixel in the kth row and Ith column: AT,,(,i, j) is the neighborhood of the pixel (i: j) defined as a rectangle whose width is the length of.the longest vehicle in the target set and whose height is that of the tallest vehicle in the target set. For the applications we have considered, the width is 7 in and the height, 3 m.
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3.2 Contrastbox, Feature 1
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The contrastbox feature measuresthe averagegrey level over a target-sized region and compares it to the grey level of the local background. We chose this feature becausemany pixels that are not on the engine or on other particularly hot portions of the target arestill somewhat warmer than the natural background. This feature has been used by a large number of authors and is calculated as
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(2)
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where 7zOUist the number of pixels in iVovt(i: j), nin is the number of pixels in n:,,(i, j), and i’Vi,(i. j) is the target-sized neighborhood defined above. The neighborhood Nout(i! j) contains all of the pixels in a larger rectangle around (i. j), except those pixels in Ni, (i, j).
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3
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3.3 Average Gradient Strength, Feature 2
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We chose the gradient-strength feature becausemanmade objects tend to show sharper internal detail than natural objects do, even when the average intensity is similar. To prevent large regions of background that show higher than normal variation from showing a high value for this feature, we subtract the averagegradient strength of the local background from the average gradient strength of the target-sized region. The feature is calculated as
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F& = -!- c Gin(i,j) - ik-
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c
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Go&, j) , (3)
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nin (k,z)EN~n(i>j)
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nout (k.Z)ENout(i,j)
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where
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G&j) = Gtn(i,j) + G&(i:j) :
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(4)
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Gfn(i:j) = c If(U) - f(G + 1>1>
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(5)
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(UW’in
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G&((i,j) = c If(U) - f(i + l:j>l :
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(6)
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(i,j)~Nin
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and GoUt(2j:) is defined similarly
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3.4 Local Variation, Feature 3 The local variation feature is calculated as
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F$ = d-
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c
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lZin (k.Z)~N,,(i,j)
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Lot&: j> ,
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(7)
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where
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L&J.) = c If@1, )- Pin(W
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63)
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(k.Z)~N,,(i,j)
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and
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3.5 How Features Were Selected
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A full description of the feature selection is outside the scopeof this report. We programmed a large number of features and calculated the value of these features over a large number of randomly selected pixels in the imagesof the training set.We also calculated the feature values at the ground truth location of the targets. We computed histograms for each feature for
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4
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both the target and background pixels and calculated a measure of separability We also calculated the correlation of the features to avoid choosing several features that are similar. Some of the features were highly correlated, which was expected becauseone of the purposes of the training was to determine which of similar features provided the greatest separability. For example, a number of contrast features were used, which normalized the target and background values by local standard deviation of the background, or of the target, or neither. Similarly, a number of gradient-strength features were calculated. The feature-pruning process was ad hoc: thus it would be reasonableto expectthat performance improvement could be obtained by the use of a more rigorous approach.
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4. Combining Features
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Each feature is normalized acrossthe image so that the featurevalue at each pixel represents the number of standard deviations that the pixel stands apart from the values for the same feature acrossthe image. Thus the feature image for the mth feature is normalized as
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where
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Pm = $ C F$
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all(k,l)
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and
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CrTn=h
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x (F,n4-pm)2.
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all(k;l)
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After normalization, the features,eachof which is calculated for eachpixel, are linearly combined into a confidence image,
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(13)
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m=O
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where the feature weights w, are determined with the use of an algorithm not described here.The confidence value of eachpixel is mapped by a scaling function S : LR-+ [0, 11,as
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S(Q) = 1 - ecYGi,j,
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where CIis a constant.
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This scaling does not change the relative value of the various pixels: it merely scalesthem to the interval [0,l] for convenience. Confidence numbers are often limited to this interval becausethey are estimates of the a posteriori probability. While this is not true for our algorithm, the use of this interval is convenient for evaluators.
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To determine the detection locations from the scaled confidence image, we choosethe pixel value with the maximum confidence value. Then a targetsized neighborhood around the image is set to zero so that the search for subsequent detections will not choosea pixel location corresponding to the same target. The processis repeated an integer number of times, where the integer is chosena priori.
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6
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5. Experimental Results
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The training results on the Hunter Liggett April 1992 ROI database are shown in the receiver operating characteristics curve in figure 1. Figure 2 shows test results on the February 1992 ROI database collected at Yuma Proving Ground (YPG), and figure 3 shows test results on the Greyling August 1992 ROI database. The Yuma test data are much more difficult because they were taken in the desert in July, so many locations in the image have a higher apparent temperature than that of the targets. The data from Greyling, Michigan are significantly easier because the temperatures are milder, and the data are comparable in difficulty to the training data. Note that no training data were used from anywhere but Hunter Liggett, so the results suggest that the algorithm is not sensitive to the training background. This is not surprising given the simplicity of the algorithm. However, learning algorithms are often sensitive to training background. Figures 4 and 5 show a sample image and the results of the algorithm on the image. The crosses in figure 5 denote the ground-truth targets, and the x’s denote the detections on the targets. Detections are designated hits if the detection center falls anywhere on the actual target: otherwise, they are designated as false alarms. The top three detections, ranked by confidence number, are designated on the image. The top two detections are hits, while the third falls near the target and is designated a false alarm. Figures 6 and 7 show another somewhat more difficult image and associated algorithm results. The top detection falls on a target in the bottom left of the image, while the second highest detection is a false alarm near the center of the image. Although the location looks like a possible target, it is merely a warm spot on the dirt road.
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The algorithm, with relatively minor modifications, has been used by the Demo III unmanned ground vehicle (UGV) program to reduce the amount of imagery that must be transmitted via radio link to a human user. It will also be used by the Sensors for UGV program at the Night Vision and Electronic Sensors Directorate to prescreen uncooled FLIR imagery and to indicate potential targets that should be looked at more closely with an active laser sensor. This algorithm has been used as a synthetic image-validation tool by measuring the performance of the algorithm in comparison to real imagery.
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Figure 1. ROC curve on 1.oo
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Hunter Liggett April
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1992 imagery.
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Horizontal axis gives
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average number of false
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alarm? per frame.
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Vertical axis is
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target-detection
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probability.
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B .2
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3 0.40 L
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0.20 -
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4.0
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False alarms per frame
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Figure 2. ROC curve on 1 .oo Yuma July 1992 imagery.
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0.80 -
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False alarms per frame
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FGirgeuyrleing3. RAOugCusct urv1e992on 1.oo
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imagery.
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False alarms per frame
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Figure 4. Easy image from Hunter Liggett April 1992 dataset.
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Fig ,ure 5. Results on imi 3ge in figure 4.
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Figure 6. Moderate image from Hunter Ligget t April 1992 datase ‘t.
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Figure 7. Results on image in figure 6.
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6. Conclusions and Future Work
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Future work might include a more systematic evaluation of potential features and an improved classification schemethat allows useful features that appear rarely to be incorporated. In a small minority of FLIR images of targets,a windshield will reflect cold sky, causing a few pixels to be extremely dark. The current scheme is not set up to incorporate such features because the weighting would be quite low since the feature is seldom useful.
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References
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1. R. Hecht-Nielsen and Y.-T. Zhou, ” VARTAC: A fovea1 active vision ATR system,” Neural Networks8, No. 7 (1995),1309-1321.
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2. M. W. Roth, ySurvey of neural network technology for automatic target recognition,” IEEE Trans.Neural Networks 1, No. 1 (1990),28-43.
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3. L. Wang, S.Der, and N. Nasrabadi, yModular neural network recognition of targets in FLIR imagery,” IEEE Pans. ImageProcessing7, No. 8 (August 1998).
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4. B. Bhanu, oAutomatic target recognition: state of the art survey,” IEEE Trans.AerospaceElect.Sys.22,No. 4 (1986)364-379.
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14
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Distribution
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Admnstr Defns Tech1Info Ctr ATTN DTIC-OCP 8725John J Kingman Rd Ste 0944 FT Belvoir VA 22060-6218
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DARPA ATTN S Welby 3701N Fairfax Dr Arlington VA 22203-1714
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Oft of the Secy of Defns ATTN ODDRE (R&AT) The Pentagon Washington DC 20301-3080
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Oft of the Secy of Defns ATTN OUSD(A&T)/ODDR&E(R) 3080Defense Pentagon Washington DC 20301-7100
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R J Trew
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AMCOM MRDEC ATTN AMSMI-RD W C McCorkle Redstone Arsenal AL 35898-5240
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US Army TRADOC Battle Lab Integration & Tech1Dirctrt
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ATTN ATCD-B FT Monroe VA 2365l-5850
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CECOM NVESD ATTN AMSEL-RD-NVOD L Garn Ste 430 ATTN AMSEL-RD-NV-VISP E Jacobs ATTN B Deaso ATTN B O’Kane ATTN D Meredith ATTN D Tidrow ATTN J Hodapp ATTN AMSRL-RD-NV-UAB C Walters
|
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Ste 401 10221Burbeck Rd FT Belvoir VA 22060
|
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US Military Acdmy Mathematical Sci Ctr of Excellence ATTN MADN-MATH MAJ M Huber Thayer Hall West Point NY 10996-1786
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Dir for MANPRINT Oft of the Deputy Chief of Staff for Prsnnl ATTN J Hiller The Pentagon Rm 2C733 Washington DC 20301-0300
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SMC/CZA 2435Vela Way Ste 1613 El Segundo CA 90245-5500
|
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TECOM ATTN AMSTE-CL Aberdeen Proving Ground MD 21005-5057
|
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US Army ARDEC ATTN AMSTA-AR-TD Bldg 1 Picatinny Arsenal NJ 07806-5000
|
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US Army Info Sys Engrg Cmnd ATTN AMSEL-IE-TD F Jenia FT Huachuca AZ 85613-5300
|
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US Army Natick RDEC Acting Tech1Dir ATTN SBCN-T P Brandler Natick MA 01760-5002
|
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US Army Simulation Train & Instrmntn Cmnd
|
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ATTN AMSTI-CG M Macedonia ATTN J Stahl 12350ResearchParkway Orlando FL 32826-3726
|
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US Army Tank-Automtv Cmnd RDEC ATTN AMSTA-TR J Chapin Warren MI 48397-5000
|
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Nav Surfc Warfare Ctr ATTN Code B07 J Pennella 17320Dahlgren Rd Bldg 1470Rm 1101 Dahlgren VA 22448-5100
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Hicks & Assoc Inc ATTN G Singley III 1710Goodrich Dr Ste 1300 McLean VA 22102
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15
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Distribution (cont’d)
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Palisades Inst for Rsrch Svc Inc ATTN E Carr 1745Jefferson Davis Hwy Ste 500 Arlington VA 22202-3402
|
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Director US Army Rsrch Lab ATTN AMSRL-RO-D JCI Chang ATTN AMSRL-RO-EN W D Bach PO Box 12211 ResearchTriangle Park NC 27709
|
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|
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US Army Rsrch Lab ATTN AMSRL-CI-AI-R Mail & Records
|
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|
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Mgmt ATTN AMSRL-CI-AP Tech1Pub (2copies) ATTN AMSRL-CI-LL Tech1Lib (2copies) ATTN AMSRL-D D R Smith ATTN AMSRL-DD J M Miller ATTN AMSRL-SE-SE H Kwon ATTN AMSRL-SE-SE LA Chan ATTN AMSRL-SE-SE N Nasrabadi ATTN AMSRL-SE-SE P Gillespie ATTN AMSRL-SE-SE S Der (20copies) ATTN AMSRL-SE-SR G Stolovy
|
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Adelphi MD 20783-l 197
|
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16
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REPORT DOCUMENTATION PAGE
|
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|
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Form Approved OMB No. 0704-0188
|
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|
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|
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Public reporting burden for this collection of informalron ISestimated to average 1 hour per response. mcludlng the bme for rewawng ~nslrucbons. searching exrsting data sources, gathering and malnlaining the data needed, and completrng and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information. including suggestions for reducing this burden, to WashIngton Headquarters Services. Directorate for lnformabon Operabons and Reports, 1215 Jefferson Davis Highway, Suite 1204. Arlington. VA 22202-4302, and to the Olflce of Management and Budget. Paperwork Reduction Project (0704~0188). Washington. DC 20503.
|
|||
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|
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|
|
I. AGENCY USE ONLY (Leave blank)
|
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|
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|
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2. REPORT DATE
|
|||
|
|
February 200 1
|
|||
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|
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|
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3. REPORT TYPE AND DATES COVERED
|
|||
|
|
Final, January 1999 to June 2000
|
|||
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|
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|
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1. TITLE AND SUBTITLE Scale-Insensitive Detection Algorithm for FLIR Imagery
|
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|
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6. AUTHOR(S) Sandor Der, Chris Dwan, Alex Chan, Heesung Kwon, and Nasser Nasrabadi
|
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5. FUNDING NUMBERS
|
|||
|
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DA PR: N/A PE: 62120A
|
|||
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|
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|
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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS
|
|||
|
|
U.S. Army Research Laboratory
|
|||
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|
|||
|
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Attn: AMSRL-SE-SE
|
|||
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|
|||
|
|
email:
|
|||
|
|
|
|||
|
|
2800 Powder Mill Road
|
|||
|
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|
|||
|
|
Adelphi, MD 20783-l 197
|
|||
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|
|||
|
|
sder@arl.army.mil
|
|||
|
|
|
|||
|
|
). SPONSORlNG/MONlTORlNG AGENCY NAME(S) AND ADDRESS
|
|||
|
|
U.S. Army Research Laboratory
|
|||
|
|
2800 Powder Mill Road Adelphi, MD 20783-l 197
|
|||
|
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|
|||
|
|
Il. SUPPLEMENTARY NOTES
|
|||
|
|
ARL PR: 1NlZMM AMS code: 622120.H16
|
|||
|
|
12a. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited.
|
|||
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|
|||
|
|
9. PERFORMING ORGANIZATION REPORT NUMBER
|
|||
|
|
ARL-TN- 175
|
|||
|
|
10. SPONSORlNGNONlTORlNG AGENCY REPORT NUMBER
|
|||
|
|
12b. DISTRIBUTION CODE
|
|||
|
|
|
|||
|
|
13. ABSTRACT (Maxmum 200 words)
|
|||
|
|
This report describes an algorithm for detecting military vehicles in FLIR imagery that will be used as a prescreener to eliminate large areas of the image from further analysis. The output is a list of likely target locations with confidence numbers to be sent to a more complex clutter-rejection algorithm for analysis. The algorithm uses simple features and is iritended to be applicable to a wide variety of target-sensor geometries, sensor configurations, and applications.
|
|||
|
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|
|||
|
|
M.SUBJECTTERTMaSrget detection, ATR
|
|||
|
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|
|||
|
|
17. SECURITY CLASSIFICATION OF REPORT
|
|||
|
|
Unclassified
|
|||
|
|
ISN 7540-01-280-5500
|
|||
|
|
|
|||
|
|
19. SECURITY CLASSIFICATION OF THIS PAGE
|
|||
|
|
Unclassified
|
|||
|
|
|
|||
|
|
19. SECURITY CLASSIFICATION
|
|||
|
|
OF ABSTRACT
|
|||
|
|
Unclassified
|
|||
|
|
|
|||
|
|
15. NUMBER OF PAGES
|
|||
|
|
22
|
|||
|
|
16. PRICE CODE
|
|||
|
|
20. LIMITATION OF ABSTRACT
|
|||
|
|
UL
|
|||
|
|
Standard Form 298 (Rev. Z-89) ‘Prescribedby ANSI3rd 239.18
|
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