This work presents an agent-based mathematical model to simulate the aggregation of carp, a harmful fish in North America. The referred mathematical model is derived from the following assumptions: (1) instead of the consensus among every carps involved in the aggregation, the aggregation of carp is completely a random and spontaneous physical behavior of numerous of independent carp; (2) carp aggregation is a collective effect of inter-carp and carp-environment interaction; (3) the inter-carp interaction can be derived from the statistical analytics about large-scale observed data. The proposed mathematical model is mainly based on empirical inter-carp force field, whose effect is featured with repulsion, parallel orientation, attraction, out-of-perception zone, and blind. Based on above mathematical model, the aggregation behavior of carp is formulated and preliminary simulation results about the aggregation of small number of carps within simple environment are provided. Further experiment-based validation about the mathematical model will be made in our future work.
Sensor-oriented vehicle tracking and analysis within a city (VTAC) plays an important role in
transportation control, public facility management and national security. This project is dedicated to
the development of a generic VTAC framework, which employs temporal and spatial dependent
partial differential equations (PDE) to formulate the expected traffic flow, through which movement
of the observed vehicles may be measured and analysis. The boundary conditions and parameters for
the traffic flow are derived from the statistical analysis about historical transportation data; the
physics domain is derived from the geographic information system. Using the artificial video data
generated by Blender as benchmark data, the VTAC framework is validated by measuring and
identifying those anomalous vehicles appeared in the video.
This paper mainly discusses the chipping and segmentation of target-of-interest (TOI) from lowresolution
gray-scale electro-optical (EO) data, which is acquired by unmanned aerial vehicle
(UAV) hovering above the city. As the processing for automatic target recognition, chipping and
segmentation of TOI consist of the following two steps: regional chipping and target
segmentation. Regional chipping is dedicated to obtaining the minimal region surrounding the
TOI so that it can be accurately and efficiently recognized. Target segmentation is dedicated to
isolating a TOI from the background and is implemented using the diffusion algorithm. The
whole work is accomplished in MATLAB and is critically assessed using the given EO data.
No-reference measurement for image quality, where an original error-free image is not provided as
reference, plays an important role in image processing and analysis. This paper mainly investigates
three no-reference image-quality metrics, which are based on the standard deviation, the maximum,
and the mean of the magnitude of the intensity gradient of pixels. Each measurement metric is
critically accessed using low resolution gray-scale images, which are acquired by unmanned aerial
vehicles cruising over the city and aim to disclose the movement of vehicles such as a semi -truck,
light colored cars, and dark colored cars, etc. The experimental results demonstrate that, compared
to alternative schemes, the standard deviation based metric provides a more accurate measurement
about the quality of images. In addition, standard deviation based scheme demonstrates superior
correlation with alternative schemes to measure the quality of images.
Crowd motion analysis covers the detection, tracking, recognition, and behavior interpretation of target group from
persistent surveillance video data. This project is dedicated to investigating a crowd motion analysis system based on a
heat-transfer-analog model (denoted as CMA-HT for simplicity), and a generic modeling and simulation framework describing crowd motion behavior. CMA-HT is formulated by coupling the statistical analysis of crowd's historical behavior at a given location, geographic information system, and crowd motion dynamics. The mathematical derivation of the CMA-HT model and the innovative methods involved in the framework's implementation will be discussed in detail. Using the sample video data collected by Central Florida University as benchmark, CMA-HT is employed to measure and identify anomalous personnel or group responses in the video.