Abstract—In this paper we propose a different approach based on a spatio-temporal feature called the Spatio Temporal Regularity Flow (SPREF) to stabilize unwanted camera motions in a video, recognize the camera motion patterns between consecutive frames and Group of Frames(GOF) and track the motion of an object in a video with the background subtracted. The method for stabilization based on Camera Motion uses the Translational Regularity flow vectors (TSPREF). In this method we fit the TSPREF vectors into parametric model to calculate the unstabilized global motion. An adaptive Gaussian smoothing method is used to smoothen the global motion followed by motion compensation to produce a stabilized sequence. Experimental results are provided and the stabilization achieved is validated using the qualitative measure Interframe Transform Fidelity (ITF).In camera motion pattern recognition we make use of TSPREF vectors to recognize the cognizant camera motion patterns. This is done for consecutive frames as well as Group of Frames(GOF)of different video sequences. In motion tracking we use the TSPREF vectors to track the moving object present in a video. The test videos taken have a still background with one or two moving objects. In all the cases we have the background subtracted from the moving object.

Index Terms—video stabilization, camera motion pattern, global motion estimation, gaussian smoothing, motion compensation, motion tracking, background subtraction, spline, regularity flow

Cite: Karthik Dinesh and Sumana Gupta, "Video Stabilization, Camera Motion Pattern Recognition and Motion Tracking Using Spatiotemporal Regularity Flow," Journal of Image and Graphics, Vol. 2, No. 1, pp. 33-40, June 2014. doi: 10.12720/joig.2.1.33-40