Abstract-This paper describes the real-time implementation of a simple and robust motion detection algorithm based on Markov random field (MRF) modeling. MRF-based algorithms often require a significant amount of computations. The intrinsic parallel property of MRF modeling has led most of implementations toward parallel machines and neural networks, but none of these approaches offers an efficient solution for real-world (i.e., industrial) applications. Here, an alternative implementation for the problem at hand is presented yielding a complete, efficient and autonomous real-time system for motion detection. This system is based on a hybrid architecture, associating pipeline modules with one asynchronous module to perform the whole process, from video acquisition to moving object masks visualization. A board prototype is presented and a processing rate of 15 images/s is achieved, showing the validity of the approach.
An automatic tracking system for rotifer swimming movements is described . The x and y coordinates of the center of gravity of the animal are stored in the computer memory every twelfth of a second .Computer programs were developed to analyze the data and to calculate the average and the standard deviation for the following parameters per unit of time : distance traveled, turning angles toward the right and toward the left, distance from the point of departure as the crow flies . Histograms are traced by the computer showing the distribution of these values, as well as their evolution with time .This experimental system is dependable . The first values which we obtained for the swimming speed of rotifers correspond well with those which are given in the literature . The other parameters had not been measured until now .
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