Three-dimensional Tracking of a Large Number of High Dynamic Objects
  from Multiple Views using Current Statistical Model
Nianhao Xie
Three-dimensional tracking of multiple objects from multiple views has a wide
range of applications, especially in the study of bio-cluster behavior which
requires precise trajectories of research objects. However, there are
significant temporal-spatial association uncertainties when the objects are
similar to each other, frequently maneuver, and cluster in large numbers.
Aiming at such a multi-view multi-object 3D tracking scenario, a current
statistical model based Kalman particle filter (CSKPF) method is proposed
following the Bayesian tracking-while-reconstruction framework. The CSKPF
algorithm predicts the objects' states and estimates the objects' state
covariance by the current statistical model to importance particle sampling
efficiency, and suppresses the measurement noise by the Kalman filter. The
simulation experiments prove that the CSKPF method can improve the tracking
integrity, continuity, and precision compared with the existing constant
velocity based particle filter (CVPF) method. The real experiment on fruitfly
clusters also confirms the effectiveness of the CSKPF method.