Detecting Moving Objects by Ant Colony System in a MAP-MRF Framework

Background subtraction is a traditional method for detecting objects in stationary background. However, this traditional method is difficult to detect objects accurately in the real world, because the background is usually cluttered and not completely static. In this paper, we propose an object detection approach using Ant Colony System (ACS) in a MAP-MRF framework. For object segmentation, a MAP-MRF framework is constructed based on the background model which is represented by the adaptive kernel density estimation method. Then, the MAP-MRF based object detection is implemented by Ant Colony System (ACS). The pheromone trail in ACS is defined reliably via the local energy of MAP-MRF. Extensive experiments show the utility and performance of the proposed approach.