An Efficient MHT Implementation Using GRASP

The multiple hypothesis tracking (MHT) approach has been proven to be successful in multiple target tracking applications, however, its computational complexity remains a major hurdle to its practical implementation. This paper presents an efficient MHT implementation, referred to as “GRASP-MHT”, which integrates a greedy randomized adaptive search procedure (GRASP) within a track-oriented MHT framework. The hypothesis generating problem arising in the MHT framework is formulated as a maximum weighted independent set problem, and a GRASP module is designed to generate multiple high-quality hypotheses. An extensive simulation study was carried out to compare the performance of the proposed GRASP-MHT against several well-known multitarget tracking algorithms, and multiple metrics were considered in order to make the performance evaluation more comprehensive. Experimental results indicate that, by efficiently generating and fusing multiple high-quality global hypotheses in the data association process, GRASP-MHT is able to achieve better overall tracking performance than other algorithms, especially in a closely-spaced multitarget scenario.