1. Brief Summary: Target Tracking from a moving platform Jackie Brosamer

2. Overview We want to track objects with a moving platform, using a map as a reference Local Association: link detected regions within a sliding window and generate tracklets Global Association: link tracklets and maintain track IDs

3. Map-Enhanced Detection Use global map such as a satellite image as reference frame for moving platform instead of first frame Reduced accumulated error Makes coordinates more meaningful (dimensions, latitude/longitude)

4. Geo-Registration First, use homography between consecutive frames Second, refine homography between image and map

8. Moving Regions For stationary, image sequence modeled at pixel level For moving, we fist model motion and then estimate background Adopt sliding window method

9. Local Data Association Maximize posterior of platforms to create tracklets Based on temporal compatibility within one track and spatial compatibility between tracks

10. Formulation Noisy Data Observations: Find cover over time: Based on –Spatial association –Temporal Association

11. MCMC Data associations Use monte carlo simulation to partition tracks Determine extension/reduction, birth/death, split/move

13. Global Tracklets Distribution Looks at longer time span to properly association tracklets with identity (esp when longer occlusion etc)