  This paper presents a novel statistical information fusion method to
integrate multiple-view sensor data in multi-object tracking applications. The
proposed method overcomes the drawbacks of the commonly used Generalized
Covariance Intersection method, which considers constant weights allocated for
sensors. Our method is based on enhancing the Generalized Covariance
Intersection with adaptive weights that are automatically tuned based on the
amount of information carried by the measurements from each sensor. To quantify
information content, Cauchy-Schwarz divergence is used. Another distinguished
characteristic of our method lies in the usage of the Labeled Multi-Bernoulli
filter for multi-object tracking, in which the weight of each sensor can be
separately adapted for each Bernoulli component of the filter. The results of
numerical experiments show that our proposed method can successfully integrate
information provided by multiple sensors with different fields of view. In such
scenarios, our method significantly outperforms the state of art in terms of
inclusion of all existing objects and tracking accuracy.
