A novel Multiple Hypothesis Testing (MHT) scheme for tracking of dim objects

Abstract

Multiple Hypothesis Tracking (MHT) is an active field for the detection and tracking of low-observable small targets. Most of MHT algorithms are based on a search method in a large tree of possible tracks in a digital image sequence. Though, processing a tree structure with a significant number of branches in MHT has been a challenging issue. Tracking high-speed objects with traditional MHT requires some presumptions which limit the capabilities of these methods. This paper presents a novel MHT system in three steps to solve this problem. The process starts with the root of each track in the video sequence followed by a Particle Swarm Optimization (PSO) search to find the optimum tracks. Iterative process of PSO tries to refine each track. In the third step, the proposed algorithm merges all of the refined tracks related to an object. Efficiency of the proposed method is presented through the figures and tables in the simulation results section.