Spatio-temporal Human Body Segmentation from Video Stream

We present a framework in which human body volume is extracted from a video stream. Following the line of object tracking-based methods, our approach detect and segment human body regions by jointly embedding parts and pixels. For all extracted segments the appearance and shape models are learned in order to automatically extract the foreground objects across a sequence of video frames. We evaluated the framework using a challenging set of video clips, consisting of office scenes, selected from Hollywood2 dataset. The outcome from the experiments indicates that the approach was able to create better segmentation than recently implemented work.