Post on 20-Jan-2016
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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