Brief Summary: Target Tracking from a moving platform

Jackie Brosamer

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

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)

Geo-Registration

• First, use homography between consecutive frames

• Second, refine homography between image and map

Moving Regions

• For stationary, image sequence modeled at pixel level

• For moving, we fist model motion and then estimate background

• Adopt sliding window method

Local Data Association

• Maximize posterior of platforms to create tracklets

• Based on temporal compatibility within one track and spatial compatibility between tracks

Formulation

• Noisy Data Observations:

• Find cover over time:

• Based on– Spatial association – Temporal Association

MCMC Data associations

• Use monte carlo simulation to partition tracks

• Determine extension/reduction, birth/death, split/move

Global Tracklets Distribution

• Looks at longer time span to properly association tracklets with identity (esp when longer occlusion etc)