This paper presents a fully automated approach to classifying deformable and non-deformable moving objects in a video surveillance scene. We estimate an object's motion using Marzat optical-flow algorithm. We filter the motion vectors and attempt to find the transformation that represents the correct mapping between the two positions. The Fundamental transformation is estimated using the Normalized Eight-Point Algorithm. We introduce a new type of graph to set the thresholds between deformable and non-deformable motion. Furthermore, we use temporal consistency to classify deformable and non-deformable objects. For experiments, we used a varied corpus of real surveillance videos. Our proposed approach for motion classification achieved a precision rate of 92 percent.