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.
Interactive Multiple-Target Tracking via Labeled Multi-Bernoulli Filters / Gostar A.K.; Rathnayake T.; Fu C.; Bab-Hadiashar A.; Battistelli G.; Chisci L.; Hoseinnezhad R.. - ELETTRONICO. - (2019), pp. 1-6. (Intervento presentato al convegno 8th International Conference on Control, Automation and Information Sciences, ICCAIS 2019 tenutosi a Chengdu, China nel 2019) [10.1109/ICCAIS46528.2019.9074567].
Interactive Multiple-Target Tracking via Labeled Multi-Bernoulli Filters
Battistelli G.;Chisci L.;
2019
Abstract
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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.