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3.3.2 Classification resultsReferences4 Multi-object particle filters4.1 Bernoulli particle filters4.1.1 Standard Bernoulli particle filters4.1.2 Bernoulli box-particle filter4.2 PHD/CPDH particle filters with adaptive birth intensity4.2.1 Extension of the PHD filter4.2.2 Extension of the CPHD filter4.2.3 Implementation4.2.4 A numerical study4.2.5 State estimation from PHD/CPHD particle filters4.3 Particle filter approximation of the exact multi-object filterReferences5 Sensor control for random set based particle filters5.1 Bernoulli particle filter with sensor control5.1.1 The reward function5.1.2 Bearings only tracking in clutter with observer control5.1.3 Target Tracking via Multi-Static Doppler Shifts5.2 Sensor control for PHD/CPHD particle filters5.2.1 The reward function5.2.2 A numerical study5.3 Sensor control for the multi-target state particle filter5.3.1 Particle approximation of the reward function5.3.2 A numerical studyReferences6 Multi-target tracking6.1 OSPA-T: A performance metric for multi-target tracking6.1.1 The problem and its conceptual solution6.1.2 The base distance and labeling of estimated tracks6.1.3 Numerical examples6.2 Trackers based on random set filters6.2.1 Multi-target trackers based on the Bernoulli PF6.2.2 Multi-target trackers based on the PHD particle filter6.2.3 Error performance comparison using the OSPA-T error6.3 Application: Pedestrian tracking6.3.1 Video dataset and detections6.3.2 Description of Algorithms6.3.3 Numerical resultsReferences7 Advanced topics7.1 Bernoulli filter for extended target tracking7.1.1 Mathematical models7.1.2 Equations of the Bernoulli filter for an extended target7.1.3 Numerical Implementation7.1.4 Simulation results7.1.5 Application to a surveillance video7.2 Calibration of tracking systems7.2.1 Background and problem formulation7.2.2 The proposed calibration algorithm7.2.3 Importance sampling with progressive correction7.2.4 Application to sensor bias estimationReferencesIndex
