Dr. Qiuming Zhu Profile

Dr. Qiuming Zhu

at Univ of Nebraska at Omaha

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Publications (3)

Proceedings Article | 14 May 2007 Paper

Towards distributed ATR using subjective logic combination rules with a swarm of UAVs

Stephen O'Hara, Michael Simon, Qiuming Zhu

Proceedings Volume 6561, 65611G (2007) https://doi.org/10.1117/12.718321

KEYWORDS: Unmanned aerial vehicles, Automatic target recognition, Logic, Video, Cameras, Video processing, Video surveillance, Sensors, Surveillance, Target recognition

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In this paper, we present our initial findings demonstrating a cost-effective approach to Aided Target Recognition (ATR) employing a swarm of inexpensive Unmanned Aerial Vehicles (UAVs). We call our approach Distributed ATR (DATR). Our paper describes the utility of DATR for autonomous UAV operations, provides an overview of our methods, and the results of our initial simulation-based implementation and feasibility study. Our technology is aimed towards small and micro UAVs where platform restrictions allow only a modest quality camera and limited on-board computational capabilities. It is understood that an inexpensive sensor coupled with limited processing capability would be challenged in deriving a high probability of detection (P_d) while maintaining a low probability of false alarms (P_fa). Our hypothesis is that an evidential reasoning approach to fusing the observations of multiple UAVs observing approximately the same scene can raise the P_d and lower the P_fa sufficiently in order to provide a cost-effective ATR capability. This capability can lead to practical implementations of autonomous, coordinated, multi-UAV operations. In our system, the live video feed from a UAV is processed by a lightweight real-time ATR algorithm. This algorithm provides a set of possible classifications for each detected object over a possibility space defined by a set of exemplars. The classifications for each frame within a short observation interval (a few seconds) are used to generate a belief statement. Our system considers how many frames in the observation interval support each potential classification. A definable function transforms the observational data into a belief value. The belief value, or opinion, represents the UAV's belief that an object of the particular class exists in the area covered during the observation interval. The opinion is submitted as evidence in an evidential reasoning system. Opinions from observations over the same spatial area will have similar index values in the evidence cache. The evidential reasoning system combines observations of similar spatial indexes, discounting older observations based upon a parameterized information aging function. We employ Subjective Logic operations in the discounting and combination of opinions. The result is the consensus opinion from all observations that an object of a given class exists in a given region.

Proceedings Article | 9 April 2007 Paper

Collective agents interpolative integral (CAII) for asymmetric threat detection

Qiuming Zhu, Stephen O'Hara, Michael Simon, Eric Lindahl, Plamen Petrov

Proceedings Volume 6571, 65710I (2007) https://doi.org/10.1117/12.718322

KEYWORDS: Data modeling, Data fusion, Probability theory, Information fusion, Systems modeling, Computing systems, Computer architecture, Logic, Mathematical modeling, Prototyping

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Proceedings Article | 28 May 2004 Paper

Median model for background subtraction in intelligent transportation system

Peijun Shi, Elizabeth Jones, Qiuming Zhu

Proceedings Volume 5298, (2004) https://doi.org/10.1117/12.525553

KEYWORDS: Intelligence systems, Data modeling, Statistical modeling, Video, Image processing, Real time image processing, Cameras, Performance modeling, Roads, Statistical analysis

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This research is generally divided into two phases: the first phase deals with background image generation and vehicle detection, the second phase deals with vehicle tracking and video handoff. In the first phase we view the image as a mixture of three data distributions: vehicle, background and shadow. Thus the problem is modeled as a mixture of Gaussian problem and our goal is to separate the background data from other data distributions. We proposed a median model and an improved median model to separate the background data from mixture data and to generate background reference images. In median model we keep track of deviation between the median and its neighbors in a reordered pixel sequence. When sample size is big enough, the reordered pixel sequence is in what we called balanced-median model. This model is indicated by a very small deviation value. In this case the median of the pixel sequence falls in background set and could be used for background estimation. When sample size is not big enough, the reordered pixel sequence is in what we called shifted-median model. This model is indicated by a much bigger deviation value. In this case the median falls out of background set and are excluded for background estimation. This median model has an impressive performance to handle slow moving or even stationary vehicles. But the time complexity is still expensive for real time image processing. The improved median model is proposed to reduce the time complexity to a reasonable level. In improved median model, we take samples in a bigger time interval to make it capable of dealing with slow moving and stationary vehicles. The sample size from experimentation is obtained as a small constant value between 5 and 20. This small sample constant size could dramatically reduce the time complexity. As a complementary to this improved median model, a mask-classified updating method is introduced to update the background image in a short term and only classified background pixels are being used for updating. Threshold, erosion, dilation and connected components labeling are used for noise removing and object labeling. After the first phase, the vehicle information is separated from image and input to the second phase for video hand-off and vehicle tracking. In the second phase, the weighted intensity information and shape information for each vehicle is scored and minimum-distance classification method is used for vehicle match. More than 400 vehicles are tested. An overall detection rate of 100% and tracking rate of 74% are obtained in this system.

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