The dynamic analysis of cell behavior is fundamental to the evaluation of the correlation between disease and abnormal cell migration. In this study, a self-regulated foraging behavior for cell tracking is proposed under the framework of dual prediction and update of an ant colony and its pheromone field. In the regulated behavior of ant foraging, three strategies are employed: range of foraging, re-sampling, based on the initial distribution of the ant colony; and the stopping criteria of the foraging. The foraging movement of an ant colony is confined to its relevant range determined by the corresponding pheromone field and dynamically varies over iterations. An initial distribution of the ant colonies at each iteration is generated by a Gaussian based re-sampling strategy to regulate an effective search in a centralized manner as a colony. An adaptive stopping criterion of foraging is put forward on the basis of Kullback-Leibler divergence of two approximately Gaussian pheromone fields between two consecutive iterations. The experimental results of the application to cell tracking show that the effectiveness of the algorithm, and demonstrate that it is better than the compared methods.