Skip to Main Content
This study presents a robust and reliable method of human posture recognition for visual surveillance systems. In order to recognise the human body, a recognition method is developed based on the skeleton of moving object. To obtain the skeleton of object, the authors describe some thinning algorithms for binary images, including one pass thinning algorithm, Zhang's thinning algorithm, Rosenfeld's thinning algorithm and a new thinning algorithm. Three performance measurements are chosen to evaluate these thinning algorithms. Comparing the performance results the authors found that the proposed thinning algorithm had managed to produce several improvements, including high thinness, connectivity, robustness to noise and low time consuming. Moreover, the skeleton obtained by the proposed thinning algorithm is one-pixel width and more smooth. Next, three different postures such as standing, bending and crawling will be estimated by using support vector machines as a classifier, which the histograms of horizontal and vertical projections are selected to define the feature. Finally, experimental results demonstrate that the human body and posture estimation algorithm have a robust and real-time performance, and is useful for the discrimination of human postures.