An efficient implementation and evaluation of Reid's multiple hypothesis tracking algorithm for visual tracking

Abstract

An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the the k-best hypotheses are determined in polynomial time using an algorithm due to Murty (1968). The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track initiation, termination and continuation are demonstrated. Continuation allows the MHT to function despite temporary occlusion of tracks. Between 50 and 150 corner features are simultaneously tracked in the image plane over a sequence of up to 60 frames. Each corner is tracked using a simple linear Kalman filter and any data association uncertainty is resolved by the MHT. Kalman filter parameter estimation is discussed and experimental results show that the algorithm is robust to errors in the motion model.