Person tracking in 3D using Kalman filtering in single and multiple camera environments - Merven, Bruno (Department of Electrical Engineering, 2024)
We present a multi-camera person tracker solution that makes use of Kalman filtering principles. The tracking system could be used in conjunction with behaviour analysis systems to perform automated monitoring of human activity in a range of different environments. Targets are tracked in a 3-D world-view coordinate system which is common to all cameras monitoring the scene. Targets are modelled as ellipsoids and their colour information is parameterised by RGB-height histograms. Observations used to update the target models are generated by matching the targets in the different views. 3-D tracking requires that cameras are calibrated to the world coordinate system. We investigate some practical methods of obtaining this calibration information without laying out and measuring calibration markers. Both tracking and calibration methods were tested extensively using 6 different single and multiple camera test sequences. The system is able to initiate, maintain and terminate the tracks of several people in cluttered scenes. However, further optimisation of the algorithm is required to achieve tracking in real time.