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CV Workshop: Multiple Target Tracking

CV Workshop: Multiple Target Tracking. Michael Rubinstein IDC 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

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CV Workshop: Multiple Target Tracking

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

  2. 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…

  3. 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

  4. 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”

  5. The Kalman Filter • Predictor(a-priori)-corrector(a-posteriori) model

  6. Tracking Multiple Targets

  7. Tracking Engine Detections classifier Update Targets Predict Targets

  8. Classifier T1 T2 T5 Y T3 T4 X

  9. Results

  10. Results

  11. Results

  12. 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

  13. Future • Tuning! • maybe learn parameters from data • Will it do better than current method? • Combine shorter, higher-accuracy tracks • Particle Filter

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