Abstract
Inspired by ant’s stochastic behavior in searching of multiple food sources, a novel ant system with two ant decision levels are proposed to track multiple cells in biological field. In the ant individual level, ants within the same colony perform independently, and ant decision is determined in probability by both its intended motion model and likelihood function. In the ant cooperation level, each ant adjusts individual state within its influence region, while the global best template at current iteration is found among all ant colonies and further utilized to update ant model probability, influence region, and the probability of fulfilling task. Simulation results demonstrate that our algorithm could automatically track numerous cells and its performance is compared with the multi-Bernoulli filtering method.
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Xu, B., Chen, Q., Lu, M., Zhu, P. (2013). Two Ant Decision Levels and Its Application to Multi-Cell Tracking. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_34
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DOI: https://doi.org/10.1007/978-3-642-38703-6_34
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