针对图像序列中多目标检测和跟踪算法结构复杂、计算量大、性能降低等问题,提出一种基于代价参考粒子滤波器组的多目标检测前跟踪(Cost-reference particle filter bank based multi-target track-before-detect,CRPFB-MTBD)算法,将多目标跟踪问题转换为序贯地检测和跟踪多个单目标的问题.首先,采用代价参考粒子滤波器组序贯地估计所有可能单目标状态序列;其次,基于所有可能单目标状态序列的欧氏距离和累积代价确定目标数量;最后,根据累积代价判断每个目标出现和消失的具体时刻.仿真实验验证了CRPFB-MTBD的优良性能,与基于传统粒子滤波的多目标检测前跟踪算法(Particle filter based multi-target track-before-detect,PF-MTBD)、基于概率假设密度的检测前跟踪算法(Probabil-ity hypothesis density based track-before-detect,PHD-TBD)和基于伯努利滤波的检测前跟踪算法(Bernoulli based track-before-detect,Bernoulli-TBD)相比,CRPFB-MTBD的目标状态序列和数量估计结果最佳,且平均单次运行时间极短.
A Multi-target Track-before-detect Algorithm Based on Cost-reference Particle Filter Bank
Aiming at the problems of complex structure,increasing computation and decreasing performance of multiple targets detection and tracking algorithms in image sequences,a cost-reference particle filter bank based multi-target track-before-detect(CRPFB-MTBD)algorithm is proposed.In this work,the target tracking problem is converted into a problem of sequentially detecting and tracking multiple single targets.First,a cost reference particle filter bank is used to sequentially estimate all possible single targets'state sequences;secondly,the number of targets is determined based on the Euclidean distances and cumulative costs of all possible single targets'state sequences;finally,the specific moment when each target appears and disappears is determined based on the cumu-lative cost.The simulation experiment verified the excellent performance of CRPFB-MTBD.Compared with the traditional particle filter based multitarget track-before-detect(PF-MTBD)algorithm,probability hypothesis dens-ity based track-before-detect(PHD-TBD),and Bernoulli filter based track-before-detect(Bernoulli-TBD),CRPFB-MTBD has the best target state sequence and quantity estimation results,and the average single running time is extremely short.
Multi-target trackingtrack-before-detect(TBD)particle filtercost-reference particle filter bank(CRPFB)filter bank
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