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

8. Classifier Y X T1 T2 T3 T4 T5

9. 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