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Video Stabilization Processing

Video Stabilization Processing. New Folder Consulting. Adaptive Tracking – Basic Idea. Analyze a single pixel in sequential frames Keep track of a suite of “concepts” that data in a pixel can be considered “background” When data does not fit any of these concepts, flag as anomalous. Anomaly!.

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Video Stabilization Processing

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  1. Video Stabilization Processing New Folder Consulting

  2. Adaptive Tracking – Basic Idea • Analyze a single pixel in sequential frames • Keep track of a suite of “concepts” that data in a pixel can be considered “background” • When data does not fit any of these concepts, flag as anomalous Anomaly!

  3. Benefits of GMM Tracking – From Goals • Goals: • Real-time detection, localization, and tracking of possible threats and obstacles • Both stationary and moving • Invariant under vehicle motion • Invariant under lighting / context changes • Robust to occlusion • Measure amount of threat to vehicle • GMM: • GMM is fast, does not distinguish between stationary and non-stationary objects • When combined with video stabilization, adaptive GMM can be robust under vehicle motion • Adaptive GMM tracking is invariant to changes in lighting • Occlusion presents complications to all algorithms. GMM can “remember location of objects” when occluded • Together with projection estimation, can estimate object size • More advanced classification of threats requires other approaches

  4. Video Stabilization • Video stabilization is important in moving camera situations; • Many techniques for video stabilization • Local, invariant features • Global feature tracking • Sub-pixel approaches • Many approaches are not relevant in forward-looking, because of vehicle motion (do not want to stabilize entire scene…) • GMM provides a map of current estimate of the road; can we use the current GMM estimate to provide some degree of video stabilization?

  5. Video Stabilization (1) 1) Assume we have a model of the road, and camera position • Currently assume camera view is red rectangle 2) Collect some data 3) Local search to find best model match to current data; update camera position

  6. Video Stabilization (2) • Background model is created as a side effect of adaptive GMM tracking • Sample background for a road with ruts, and burms to the left and right sides • Optimal local camera position cab be found using log-likelihood of data | position Tire Tracks Left Side Right Side

  7. Object Identification and Tracking

  8. Ongoing Work: February 2010 • New Folder: • Investigate improvements to GPR processing – • Pre-Screening • Pre-Processing • Background modeling & removal • Post-Processing • KSUM improvements • Scoring & evaluation code New Folder Consulting

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