A multiple target detection algorithm based on Imperialist Competitive Algorithm
Mohammad Rismanbor, Karim Faez · 2011
In this paper in order to introduce multiple target detection method. We combination histogram feature and Imperialist Competitive Algorithm (ICA). We use histogram feature because it is robust to the target rotation and scales. To overcome the computation problem of pixel by pixel searching, ICA is employed. Another advantage of ICA is that if several targets in the image or frame exist, we will be able to detect simultaneously all targets in the frame. Then we apply a threshold in order to remove weak empires which belong to objects which have similarity to targets. Then clustering empires based on the distance and selecting most powerful empire of each cluster as one of the targets contained in frame, therefore we can detect all targets existing in the frame. Finally we compare ICA method with PSO (Particle Swarm Optimization) method and show that ICA is faster and more accurate than PSO in the field of target detection.