AERIAL SURVEILLANCE FOR VEHICLE DETECTION USING DBN AND CANNY EDGE DETECTOR

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixel wise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixel wise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and non-vehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixel wise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles

Feasibility Study to Determine the Economic and Operational Benefits of Utilizing Unmanned Aerial Vehicles (UAVs)

This project explored the feasibility of using Unmanned Aerial Systems (UASs) in Georgia Department of Transportation (GDOT) operations. The research team conducted 24 interviews with personnel in four GDOT divisions. Interviews focused on (1) the basic goals of the operators in each division, (2) their major decisions for accomplishing those goals, and (3) the information requirements for each decision. Following an interview validation process, a set of UASs design characteristics that fulfill user requirements of each previously identified division was developed. A “House of Quality” viewgraph was chosen to capture the relationships between GDOT tasks and potential UAS aiding those operations. As a result, five reference systems are proposed. The UAS was broken into three components: vehicle, control station, and system. This study introduces a variety of UAS applications in traffic management, transportation and construction disciplines related to DOTs, such as the ability to get real time, digital photographs/videos of traffic scenes, providing a "bird’s eye view" that was previously only available with the assistance of a manned aircraft, integrating aerial data into GDOT drawing software programs, and dealing with restricted or complicated access issues when terrain, area, or the investigated object make it difficult for GDOT personnel to conduct a task. The results of this study could lead to further research on design, development, and field-testing of UAVs for applications identified as beneficial to the Department.Georgia Department of Transportatio