Kalman Filters for Object Tracking

A Kalman filter is a recursive state estimator for a system modeled with linear dynamics and Gaussian noise. It keeps an estimated state and its uncertainty. In a tracking example, the state might contain an object’s image position and velocity. At each time step, a prediction step advances that state through a motion model and propagates uncertainty. When a detector supplies a measurement, a correction step combines the measurement with the prediction according to their uncertainties. This is useful when detections are noisy or arrive intermittently: the predicted state can bridge a short gap and the corrected estimate can be less jittery than raw detections. But a Kalman filter is not a visual detector. It needs a measurement source, a suitable state representation, and realistic noise assumptions. It does not by itself decide which of several detections belongs to which track. Multi-object tracking adds data association, track creation and deletion, and often appearance cues or gating rules. If the motion is strongly nonlinear, a basic linear filter may be a poor fit; extended or unscented filters and other estimators use different assumptions. Evaluate the complete tracker on sequences that include missed detections, abrupt turns, camera motion, and occlusion. Measure localization error, track continuity, identity switches, and latency. Tune uncertainty using representative data and preserve a way to recover when a track diverges rather than treating a predicted position as a confirmed observation.