Multiple target tracking using Sequential Monte Carlo Methods and statistical data association

This paper presents two approaches for the problem of multiple target tracking (MTT) and specifically people tracking. Both filters are based on sequential Monte Carlo methods (SMCM) and joint probability data association (JPDA). The filters have been implemented and tested on real data from a laser measurement system. Experiments show that both approaches are able to track multiple moving persons. A comparison of both filters is given and the advantages and disadvantages of the two approaches are presented.

Authors

• Oliver Frank
• Juan I. Nieto 0001
• José E. Guivant
• Steve Scheding

Keywords

• Target tracking
• Filters
• Radar tracking
• State estimation
• Noise measurement
• Time measurement
• Vehicle dynamics
• Monitoring
• Recursive estimation
• Equations
• Particle Filter
• Multiple Tracking
• Multiple Target Tracking
• Time Step
• Computational Cost
• Target Sequence
• Discrete-time
• State Space
• Control Input
• Estimation Problem
• Process Noise
• Critical Situations
• Past Observations
• True Measure
• Joint Association