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In this paper we present a robust method for background subtraction from a fixed camera in video surveillance system. The background subtraction is an important part of object tracking and many algorithms have been proposed for decades. Mixture of Gaussian for those in this paper is very famous and used widely. We present the robust method that can adapt the background model to various situations...
This paper attempts to find moving objects by subtracting the background images from static single camera video sequences in security systems. It aims to improve the background subtraction techniques for indoor video surveillance applications. For dynamic video sequences, the object focus and object tracking are very difficult due to changing of various parameters such as camera noise, illumination...
This letter presents a new method for background subtraction and shadow removal for grayscale video sequences. The background image is modeled using robust statistical descriptors, and a noise estimate is obtained. Foreground pixels are extracted, and a statistical approach combined with geometrical constraints are adopted to detect and remove shadows.
A statistical change detector, implemented as a zero-latency finite-memory filter, is used to identify anomalies in temporal pixel statistics. An F-distributed test statistic is computed for each pixel and used in a hypothesis test. The tracker, with automatic track initiation and termination, uses a low-complexity pairwise joint probabilistic data association (JPDA) algorithm, which has been restricted...
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