Supervisor: Nakhmani Arie Semester: Winter 2007 Target Recognition Harmatz Isca

Project goals Create a target classification system based on dimension reduction, using the targets contour. No dependence on illumination and color Universal method works on all target types and sizes Fast learning for new targets Low computational needs The dimension reduction algorithm can be adopted to work on all types of data.

Motivation Tracking people ATR– automatic target recognition Find suspects in given areas Look for specific characteristics of targets

Method Result Post processing Post processing Dimension Reduction Dimension Reduction Snakes Change Detection Change Detection

Working Database 475 images 475 images 2176 snakes found 2176 snakes found The snakes were divided into 3 types: The snakes were divided into 3 types: Real (339) – a snake of a person Partial (155) – a snake were the person was partially hidden, or a clear silhouette was not detected False (1682) – a snake of a random change in the image

Get several reference images Create average reference image = Background Image Subtract the background from the image Find changed pixels Change detection Detect changes in image

Snakes Level Set Evolution Without Re-initialization: A New Variational Formulation Chunming Li, Chenyang Xu, Changfeng Gui, and Martin D. Fox CVPR 2005

Dimension reduction Select target snake Transform snake to vector Add snake vector to vector database Perform dimension reduction on vectors Displaying dimension reduction results in graph X Y Database LLE or PCA

For every snake in database: For every snake in database: Find K nearest neighbors { z 1:K } Find weight W ij for every neighbor z j Compute the projection to lower space where weighted distance from neighbors is minimum Local Linear Embedding (LLE)

Principal components analysis (PCA) Calculate the covariance matrix of database Calculate eigenvectors (ordered by eigenvalues) Find snakes representation with eigenvectors

LLE vs PCA LLE Non-linear embedding Local Keeps subspace with best local linear structure Assumes local linearity PCA Linear embedding Global Keeps subspace with best variance of data Assumes global linearity

Results LLE

Results PCA

Post-processing Steps taken to achieve better separation between false and true snakes Compactness: Area/Perimeter² Adaptive Database Target Tracking

Compactness Grade = area/perimeter 2

Dimension Reduction and Compactness Grade = Grade PCA. Grade Compactness

Adaptive Database Unsupervised Snakes matching a certain grade level are added to the database. Snakes in database with low grades are removed. The algorithm was applied for every movie separately

Adaptive Database

Tracking Define Target of interest For every next image: Define search region If “good” snake is found, then Set target to found snake Set target to found snakeElse Increase search area Increase search area Move to next image

Tracking Results

Conclusions Dimension reduction was used to find people in images. The method works well on clear silhouettes. Different post-processing methods used to improve results, each with its own pros and cons. The method works with a small database (20 snakes) and can be adopted for real time work.

Feature Directions Occluded target support Occluded target support Improve target tracking Improve target tracking Multiple targets Kalman / Particle filters Target specific database Adaptive grade threshold Adaptive grade threshold Improved snakes Improved snakes