Kernel-based learning of birth process from evolving spatiotemporal RFS data stream in SMC[sbnd]CPHD filter for multi-target tracking

Abstract

This paper presents a kernel-based framework to estimate the birth process in the sequential Monte Carlo cardinalized probability hypothesis density (SMCCPHD) filter from the evolving spatiotemporal birth random finite set data stream that can enhance the performance of the SMCCPHD based trackers when tracking targets with no a priori information on where and when targets can appear. The time-varying spatial distribution of birth events is modelled as a map with adaptive grid points and their corresponding density values. While the spatial distribution of birth density is estimated from the elements of the birth random finite sets (RFSs), the time-varying birth cardinality distribution is estimated from the cardinality of the birth RFSs. The corresponding estimations are eventually used by a SMCCPHD based tracker to adjust its filtering strength spatially and temporally for target tracking. Experimental results on both the synthetic and real video surveillance datasets show that the proposed algorithm can improve the tracking accuracy by more precisely eliminating clutters and more effectively capturing newborn targets.