Interactive Multiple-Target Tracking via Labeled Multi-Bernoulli Filters
Amirali Khodadadian Gostar, Tharindu Rathnayake, Chunyun Fu, Alireza Bab‐Hadiashar, Giorgio Battistelli, Luigi Chisci, Reza Hoseinnezhad · 2019
In many cases, the multi-target tracking system is essential for realizing the current state of an environment. The standard multi-target tracking algorithms assume that each target state evolves independently and regardless of other targets' states. However, in a real scenario this assumption does not hold in that the motion of any target is dependent on other targets. This paper proposes a new mathematical solution for multi-target tracking system with interacting targets. In the proposed method the prediction operation of the labeled multi-Bernoulli filter is extended to incorporate all possible interactions between targets. The results show that in scenarios where the assumption of a standard motion model is violated, the proposed method achieves higher accuracy for the state estimation of the targets. Also, it shows better performance for estimating the identity of the targets.