Multi-Tracker Partition Fusion
We propose a decision-level approach to fuse the output of multiple trackers based on their estimated individual performance. The proposed approach is composed of three main steps. First, we group trackers into clusters based on the spatiotemporal pair-wise correlation of their short-term trajectories. Then, we evaluate performance based on reverse-time analysis with an adaptive reference frame and define the cluster with trackers that appear to be successfully following the target as the on-target cluster. Finally, the state estimations produced by trackers in the on-target cluster are fused to obtain the target state. The proposed fusion approach uses standard tracker outputs and can therefore combine various types of trackers. We tested the proposed approach with several combinations of state-of-the-art trackers and also compared it with individual trackers and other fusion approaches. The results show that the proposed approach improves the state estimation accuracy under multiple tracking challenges.