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We explore spatial temporal correlation for reliable object detection and reconstruction of its possible trajectory in progressing series of time.
We define a foreground region as a reliable object, if the region is detected and matched in subsequent frames.
After the GMM-based background subtraction it has to be decided which of the detected foreground pixels in the binary mask represent true and reliable object regions.
A reliable object classification and intruder detection technique is needed to be performed by the sink node so that object detection would be accurate.
In this paper, we propose a novel secure and reliable object tracking protocol that considers security and object tracking tasks simultaneously.
The detection result of a reliable object already being tracked is compared to the tracking result of GMM-SAMT to check if the detection result is still valid; see Figure 1.
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To avoid a tracking of these wrong detection results we have to distinguish between reliable (true objects) and nonreliable objects (uncovered background).
Hence, we can now use the size for detecting reliable objects.
To determine reliable objects among the detected foreground regions the obtained binary masks are transformed using the corresponding homography matrix.
In Section 2 the detection of moving foreground regions is explained while Section 3 describes the determination of reliable objects among the detected foreground regions.
The detection unit is composed of a Gaussian mixture model- (GMM-) based moving foreground detection method followed by a method for determining reliable objects among the detected foreground regions using a projective transformation.
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