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.

提出了一种基于当前统计模型的卡尔曼粒子滤波（CSKPF）算法，用于多视角多目标三维跟踪场景，通过预测目标状态、估计目标状态协方差、抑制测量噪声以提高粒子采样效率，与现有基于恒定速度的粒子滤波（CVPF）方法相比，模拟实验和果蝇集群真实实验都证实了 CSKPF 方法在跟踪的完整性、连续性和精度方面的有效性。