A boosting framework for human posture recognition using spatio-temporal features along with radon transform

Automatic human posture recognition in surveillance videos has real world applications in monitoring old-homes, restoration centers, hospitals, disability, and child-care centers. It also has applications in other areas such as security and surveillance, sports, and abnormal activity recognition. Human posture recognition is a challenging problem due to occlusion, background clutter, illumination variations, camouflage, and noise in the captured video signal. In the current study, which is an extension of our previous work (Ali et al. Sensors, 18(6):1918, 2018), we propose a novel combination of a number of spatio-temporal features computed over human blobs in a temporal window. These features include aspect ratios, shape descriptors, geometric centroids, ellipse axes ratio, silhouette angles, and silhouette speed. In addition to these features, we also exploit the radon transform to get better shape based analysis. In order to obtain improved posture classification accuracy, we used J48 classifier under a boosting framework by employing the AdaBoost algorithm.The proposed algorithm is compared with eighteen existing state-of-the-art approaches on four publicly available datasets including MCF, UR Fall detection, KARD, and NUCLA. Our results demonstrate the excellent performance of the proposed algorithm compared to these existing methods.