No abstract

No abstract

The aim of the current study is to use the FIS Fuzzy Inference System method to determine motion in digital surveillance systems. In the analysis, there are several methods used such as segmenting, sorting, evaluating and showing the results. After capturing an online video stream with an average of 1 frame/sec, the goal of the proposed method is to translate these frames to their corresponding representation of pixels and then use these frames as inputs for the job. The output is evaluated based on these inputs. The idea is to compare the average pixel representation of the current frame boundaries (column vectors) with the corresponding column vectors in the next frame, in order to find out whether there is any motion detected by comparing the average of the calculated column vector for the ith frame with the corresponding column vector in the i+1th frame. This operation leads to extracting 8 averages and it is considered as inputs to the fuzzy inference method. There is one output that will detect whether there is any motion detected or not. By designing a set of rules and then analyzing the results, a comparison of the averages is held.

The fitness's dynamic and static tests require the person being tested to run or walk as far as possible in a determined period, depending on some main factors it can be decide the status of the athletic. This work aims to create databases dedicated for dynamic and static fitness tests utilizing fuzzy logic to estimates athletic tests in different ages. The procedure of this work is divided into two steps, first determining the factors for processing, the second is the databases and FIS construction. The determined databases are considered as an inputs and output for proposed fuzzy logic system. There are two inputs (Age, Distance) and one output (Status), the membership functions for the first input (Age) are (Young Adults_A, Young Adults_B, Young Adults_C, Young Adults_D, Middle Aged_A, Middle Aged_B), the membership functions for the next input (Distance) are (Very Short, Short, Medium, Long, Very long), while the membership functions of the determined output (Status) are (Very Good, Good, Accepted, Bad, Very Bad). The procedure for creating proposed fuzzy logic structure is repeated twice, one for male and other for female.