CV Workshop: Multiple Target Tracking Michael Rubinstein IDC Jan. 27 2009

CV Workshop:Multiple Target Tracking

Michael RubinsteinIDC

Jan. 27 2009

Target Tracking and MTT The problem:

Identifying moving objects

Practically: Input: Detection/Sensor (noisy) measurements Estimating the most probable measurement at time k from

measurements up to time k

Applications: Computer vision (tracking), robotics, control theory,

astronomy, ballistics (missiles), econometrics (stocks), etc…

MTT in Dense Crowd Detection of head tops (+ height) using

multiple cameras Current method

Heuristic, but works well Offline

In this work: Mathematical model Online

Eshel & Moses, 2008

The Kalman Filter Assumptions:

The process is modeled by a linear system. e.g. xk=xk-1+vt

Measurement (and prediction) noise is normally distributed

Result: Analytic solution! Unique “best estimate”

The Kalman Filter Predictor(a-priori)-corrector(a-posteriori)

model

Tracking Multiple Targets

Tracking Engine

classifier

UpdateTargets

PredictTargets

Detections

Classifier

Y

X

T1

T2

T3

T4

T5

Results

Results

Results

Until now What have I learned about this problem?

It’s a problem… Many parameters, should be set as accurately as

possible Need labeled data

Pros Sound model Linear system + normal estimation might be

sufficient Not much references for dense tracking

Future Tuning!

maybe learn parameters from data Will it do better than current method? Combine shorter, higher-accuracy tracks Particle Filter