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This paper proposes a semi-supervised intelligent visual surveillance system to exploit the information from multi-camera networks for the monitoring of people and vehicles. Modules are proposed to perform critical surveillance tasks including: the management and calibration of cameras within a multi-camera network; tracking of objects across multiple views; recognition of people utilising biometrics...
A new method for real-time detection and tracking of multiple moving vehicles from traffic video is proposed. This method first uses MoG and texture based model to extract foreground from the scene, then detect moving targets using a modified version of timed motion history image (tMHI), and finally uses Kalman prediction filter to track these targets, which the full moving trajectories of the targets...
Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications. We develop an efficient adaptive segmentation algorithm for color video surveillance sequence in real time with non-stationary background; background is modeled using partial correlation coefficient using pixel-level based approach. At runtime, segmentation is performed by checking...
We designed a real time moving object extraction and behavior analysis system to detect if any abnormal behavior like intrusion, halt, and wall climbing happened. Much work in the area of motion based video object segmentation is being done in the pixel domain which exploits the visual attributes and motion information. Given most existing images and videos stored in the compressed form, the specific...
Active contour model and mean shift are both motion detection algorithms. Each of them has its own merits and shortcomings. An active contour tends to be tracked by noise points and results in a false boundary. A mean shift vector always points to the edge area when the start point is around the object With initial curves given near the objects in each image automatically, we presented a new motion...
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