Implementation of real time reconfigurable embedded architecture for people counting in a crowd area

International audienceWe propose a feature fusion method for crowd counting. By image feature extraction and texture feature analysis methods, data obtained from multiple sources are used to count the crowd. We count people in high density static images. Most of the existed people counting methods only work in small areas, such as office corridors, parks, subways and so on. Our method uses only static images to estimate the count in high density images (hundreds or even thousands of people), for example, large concerts, National Day parade. At this scale, we can't rely on only one set of features for counting estimation. Therefore, we use multiple sources of information, namely, HOG and LBP. These sources provide separate estimates and other combinations of statistical measurements. Using the support vector machine (SVM) classification technique, and regression analysis, we count the crowd with high density. The method gives good results in crowded scenes