Abstract
Aiming at the data association problem of multi-target tracking, a data association method for multi-target tracking is given based on an improved Ant Colony algorithm (IACA) in this paper. Firstly, according to the characteristics of multi-target data association problem, it turns the issue to combinatorial optimization problems. Secondly, to address the shortcomings of the ant colony algorithm, which tends to fall into local optimum, the elite strategies and sorting strategies is introduced into the traditional Ant Colony algorithm. In addition, for the problem of ant colony algorithm combined parameters selection, a model of optimum combined parameters selection based on game theory is introduced into the proposed IACA algorithm. Finally, the improved ant colony algorithm is applied to multi-target tracking data association problem. Simulation results show that IACA has better performance in solving multi-target data association problem than ACA and the ant colony system incorporating the game parameter estimation model is more practical in multi-target tracking.
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Di, Y., Zhou, G., Tan, Z., Li, R., Wang, Z. (2023). A Novel Data Association Method for Multi-target Tracking Based on IACA. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13969. Springer, Cham. https://doi.org/10.1007/978-3-031-36625-3_6
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