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

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Presentation transcript:

CV Workshop: Multiple Target Tracking Michael Rubinstein IDC Jan

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. x k =x k-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 Update Targets Predict Targets Detections

Classifier Y X T1 T2 T3 T4 T5

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