Identification of vehicle targets from low-cost ladar-seeker imagery

Wellfare, Michael R.; Holmes, Violeta; Pohlman, Scott; Geci, Duane; Norris-Zachery, Karen; Patton, R.J.

doi:10.1117/12.243558

Cited by 2 publications

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“…According to Zhang [27], the Hough transform is applicable when the amount of data is much larger than the number of parameters. In our case the amount of data is limited, and less than in [26], and the Hough transform will not be applicable.…”

Section: Fitting Of Buildings and Vehiclesmentioning

confidence: 97%

“…The classi…cation can be followed by an identi…cation step, where the objects are separated within their group (Volvo, Saab etc.). Wellfare et al [26] have developed a set of detection and vehicle identi…cation algorithms based on data from (scanning) laser radar systems.…”

Section: The Object Classi…cation Processmentioning

confidence: 99%

“…Second, the amount of measurements on the object is smaller, as vehicles normally are smaller than buildings. In [26] the object edge estimation is made using a modi…ed Hough transform, see e.g. [10].…”

Section: Fitting Of Buildings and Vehiclesmentioning

confidence: 99%

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Vehicle size and orientation estimation using geometric fitting

Groenwall

Millnert

2001

SPIE Proceedings

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Over the years imaging laser radar systems have been developed for both military and civilian applications. Among the applications we note collection of 3D data for terrain modelling and object recognition. One part of the object recognition process is to estimate the size and orientation of the object. This thesis concerns a vehicle size and orientation estimation process based on scanning laser radar data. Methods for estimation of length and width of vehicles are proposed. The work is based on the assumption that from a top view most vehicles' edges are approximately of rectangular shape. Thus, we have a rectangle …tting problem. The …rst step in the process is sorting of data into di¤erent lists containing object data and data from the ground closest to the object. Then a rectangle with minimal area is estimated based on object data only. We propose an algorithm for estimation of the minimum rectangle area containing the convex hull of the object data. From the rectangle estimate, estimates of the length and width of the object can be retrieved. The …rst rectangle estimate is then improved using least squares methods based on both object and ground data. Both linear and nonlinear least squares methods are described. These improved estimates of the length and width are less biased compared to the initial estimates. Three algorithms are evaluated; a minimum rectangle estimator proposed by the author, the mixed LS-TLS algorithm and a quasi-Newton algorithm. The algorithms are applied to both simulated and real laser radar data. The thesis ends with a discussion of the assumptions that this work is based on and some suggestions of future work. i Second, I would like to thank the Swedish Defence Research Establishment (FOA) for letting me do this work, especially Dr. Ove Steinvall, head of the Laser Systems department. No matter how tight his schedule is, he always has time to discuss present and future laser radar systems, both regarding sensors and signal processing. I would also like to thank the head of the Sensor Division, Prof. Svante Ödman, for letting me do this work and supporting me …nancially so that it became possible. Most of the work have been done in projects run by Prof. Erland Jungert, with whom I have stimulating discussions concerning object detection and classi…cation and on geometric …tting. I am very grateful to him and to the other project members. I would also like to thank Prof. Lennart Ljung and the Automatic Control group, Linköping University, for providing me the opportunity to join the group.

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Section: Fitting Of Buildings and Vehiclesmentioning

confidence: 97%

Section: The Object Classi…cation Processmentioning

confidence: 99%

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Vehicle size and orientation estimation using geometric fitting

Groenwall

Millnert

2001

SPIE Proceedings

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<title>Characterization of articulated vehicles using ladar seekers</title>

Wellfare

Norris-Zachery

1997

SPIE Proceedings

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Many vehicle targets of interest to military Automatic Target Recognition (ATR) possess articulating components: that is, they have components that change position relative to the main body. Many vehicles also have multiple configurations wherein one or more devices or objects may be added to enhance specific military or logistical capabilities.As the expected target set for military ATR becomes more comprehensive, many additional articulations and optional components must be handled. Mobile air defense units often include moving radar antennae as well as turreted guns and missile launchers. Surface-to-Surface Missile (SSM) launchers may be encountered with or without missiles, and with the launch rails raised or lowered. Engineer and countermine vehicles have a tremendous number of possible configurations and even conventional battle tanks may vary items such as external reactive armor, long-range fuel tanks, turret azimuth, and gun elevation.These changes pose a significant barrier to the target identification process since they greatly increase the range of possible target signatures. When combined with variations already encountered due to target aspect changes, an extremely large number of possible signatures is formed. Conventional algorithms cannot process so many possibilities effectively, so in response, the matching process is often made less selective. This degrades identification performance, increases false alarm rates, and increases data requirements for algorithm testing and training. By explicitly involving articulation in the detection and identification stages of an ATR algorithm, more precise matching constraints can be applied, and better selectivity can be achieved. Additional benefits include the measurement of the position and orientation of articulated components, which often has tactical significance.In this paper, the results of a study investigating the impact of target articulation in ATR for military vehicles are presented. 3-D ladar signature data is used. An algorithmic solution is proposed and directions for further research are noted.

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Identification of vehicle targets from low-cost ladar-seeker imagery

Cited by 2 publications

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Vehicle size and orientation estimation using geometric fitting

Vehicle size and orientation estimation using geometric fitting

<title>Characterization of articulated vehicles using ladar seekers</title>

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