Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking

Bo Li,Xiaohui Li,Huawei Yi

doi:10.1177/0020294019877494

Abstract

Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.

Highlights

Multi-target tracking is to estimate both state and cardinality of mobile targets using available measurements contaminated by random clutters.[1,2,3] With respect to high nonlinearity and varying times in actual video surveillance field, multi-target tracking has become an important technology for autonomous systems.The measurement uncertainties and filtering efficiency are always our concerns
In section ‘‘Methodology,’’ we propose the improvements of the unscented transformation (UT)-based particle cardinalized probability hypothesis density (PCPHD) filter and illustrate its filtering process
We present a lemma of the optimized Kalman filter (KF) gain by defining a set of qÀdimensional sigma points f§(kxÀ)1gqx = 0. assigned to xMlk,À(oi1) raeonvdePr,kÀv1(kx,À)r1esapnedctiyv(kexÀl)y1

Summary

Introduction

Multi-target tracking is to estimate both state and cardinality of mobile targets using available measurements contaminated by random clutters.[1,2,3] With respect to high nonlinearity and varying times in actual video surveillance field, multi-target tracking has become an important technology for autonomous systems.The measurement uncertainties and filtering efficiency are always our concerns. Multi-target tracking is to estimate both state and cardinality of mobile targets using available measurements contaminated by random clutters.[1,2,3] With respect to high nonlinearity and varying times in actual video surveillance field, multi-target tracking has become an important technology for autonomous systems. Once the assumptions are unexpected, its tracking performance severely declines To deal with this problem, an adaptive UKF for actual state and parameter estimation was derived in Song.[9] Subsequently, a new adaptive UKF and its typical strategy were summarized in Pan et al.[10]. Z at time k can be written as zk = Hkxk + ek ð2Þ where zk is the measurement matrix, Hk is the measurement transfer matrix, and ek is the measurement noise vector with the Gaussian distribution N (0, Rk). Considering the uncertainty and randomness characterized by equations (1) and (2), we will apply the CPHD filter to track multi-target

Methods

Results

Conclusion