A realtime system for shrug detection is discussed in this paper. The system is automatically initialized by a face detector based on Ada-boost, After frontal face is localized by the face detector, shoulder position is detected by fitting a parabola to the nearby horizontal edges using weighted Hough Transform. Since shrug is an action which is defined not only by the distance between face and shoulder but also the relative temporal-spacial changing between them, we propose a parameterizing scheme using two different parabolas, named as "Stable Parabola" (SP) and "Transient Parabola" (TP) to characterize the action shrug. Stable Parabola represents the mean shoulder position over a long time duration, while Transient Parabola represents the mean shoulder position of a very short time duration. By using this scheme (only 6 dimensions), we avoid the high dimensional representation of the temporal process - shrug, and therefore make the realtime implementation possible. The shrug detector is then trained in the parameter space using Fisher Discriminant Analysis (FDA). The experiments show that the proposed shrug detector is able to not only detect the shrug action correctly and efficiently (in realtime), but also tolerate the large in-class variation caused by different subject, different action speed, illumination, partial occlusion, and background clutter. So the proposed realtime shrug detector is promising in video analysis under an uncontrolled environment.