Multiple Hypothesis Clustering, Multiple Frame Assignment, Tracking

摘要

Tracking and initiating large numbers of closely spaced objects can pose significant real-time challenges to current state-of-the-art tracking systems. Cluster or group tracking has been suggested to reduce the computational complexity when closely spaced targets move with similar dynamical properties. While modern individual object tracking systems make association decisions over multiple frames of data, most cluster tracking systems make single-frame clustering decisions. In this paper we illustrate an extension of multiple frame assignment (MFA) individual object tracking to multiple frame cluster MFA tracking. In our approach, multiple single-frame clustering hypotheses are formed and the best clustering is selected over multiple frames of data. In recent work we formulated multiple frame cluster tracking assignment problems and demonstrated a single-frame cluster MFA tracking architecture. The work discussed in this paper extends the previous work and illustrates a multiple hypothesis clustering, multiple frame assignment (MHC-MFA), tracking system. We present simulations studies that motivate the benefits of the multiple frame cluster tracking approach over single-frame cluster tracking and discuss the computational efficiency of the multiple frame cluster tracking approach.