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- W2036574255 abstract "Object detection and tracking using computer vision (CV) techniques have been widely applied to sensor fusionapplications. Many papers continue to be written that speed up performance and increase learning of artificiallyintelligent systems through improved algorithms, workload distribution, and information fusion. Militaryapplication of real-time tracking systems is becoming more and more complex with an ever increasing need offusion and CV techniques to actively track and control dynamic systems. Examples include the use of metrologysystems for tracking and measuring micro air vehicles (MAVs) and autonomous navigation systems for controllingMAVs. This paper seeks to contribute to the determination of select tracking algorithms that best track a movingobject using a pan/tilt/zoom (PTZ) camera applicable to both of the examples presented. The select featuregeneration algorithms compared in this paper are the trained Scale-Invariant Feature Transform (SIFT) andSpeeded Up Robust Features (SURF), the Mixture of Gaussians (MoG) background subtraction method, the Lucas-Kanade optical flow method (2000) and the Farneback optical flow method (2003). The matching algorithm used inthis paper for the trained feature generation algorithms is the Fast Library for Approximate Nearest Neighbors(FLANN). The BSD licensed OpenCV library is used extensively to demonstrate the viability of each algorithm andits performance. Initial testing is performed on a sequence of images using a stationary camera. Further testing isperformed on a sequence of images such that the PTZ camera is moving in order to capture the moving object.Comparisons are made based upon accuracy, speed and memory." @default.
- W2036574255 created "2016-06-24" @default.
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- W2036574255 date "2012-06-08" @default.
- W2036574255 modified "2023-09-23" @default.
- W2036574255 title "Determination of feature generation methods for PTZ camera object tracking" @default.
- W2036574255 doi "https://doi.org/10.1117/12.918452" @default.
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