A Multi-target Track-before-detect Algorithm Based on Cost-reference Particle Filter Bank
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摘要: 现有从图像序列中检测和跟踪低信噪比、时变数量多目标方法将多目标视为一个整体, 因此, 随着目标数量的增加, 会出现算法结构复杂、计算量增大、性能下降等问题. 针对上述问题, 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估计多个单目标问题. 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, 基于欧氏距离合并或删减多个单目标状态, 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(Probability 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的目标状态和数量估计结果最佳, 且平均单次运行时间极短.
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关键词:
- 多目标跟踪 /
- 检测前跟踪 /
- 粒子滤波 /
- 代价参考粒子滤波器组 /
- 滤波器组
Abstract: The state-of-the-art algorithms for detecting and tracking multi-targets in image sequence suffer common problems such as complex structure, heavy computational load and poor performance. To make improvement, a novel cost-reference particle filter bank based multi-target track-before-detect (CRPFB-MTBD) algorithm is proposed. In CRPFB-MTBD, the problem of multiple target detection and track is converted to the problems of sequentially detecting and tracking single target. First, sequentially estimate the states of multiple targets by using cost-reference particle filter bank. Second, determine the number of targets based on Euclidean distance. Third, determine the time instants of target presence and absence by using cumulative sum of costs. Simulation results show that, compared with the traditional particle filter based multi-target track-before-detect (PF-MTBD) algorithm, probability hypothesis density based track-before-detect (PHD-TBD), and Bernoulli filter based track-before-detect (Bernoulli-TBD), the proposed has the best performance in state and target number estimations, and average single running time. -
图 8 当SNR = 10 dB, 3个目标时, 4种方法的OSPA
Fig. 8 Comparison of OSPAs resulted from 4 algorithms when SNR = 10 dB and 3 targets
图 9 当SNR = 8dB, 3个目标时, 4种方法的OSPA
Fig. 9 Comparison of OSPAs resulted from 4 algorithms when SNR = 8 dB and 3 targets
图 10 当SNR = 8 dB, 3个目标时, CRPFB-MTBD的目标状态估计结果
Fig. 10 State estimation of CRPFB-MTBD when SNR = 8 dB and 3 targets
图 11 当SNR = 8 dB, 3个目标时, CRPFB-MTBD的目标数量估计结果
Fig. 11 Estimation of target number provided by CRPFB-MTBD when SNR = 8 dB and 3 targets
图 12 当SNR = 6 dB时, 目标数量对CRPFB-MTBD性能的影响
Fig. 12 Impact of target number on CRPFB-MTBD when SNR = 6 dB
图 13 当SNR = 6 dB, 3个目标时, CRPF数量对CRPFB-MTBD性能的影响
Fig. 13 Impact of the number of CRPFs on CRPFB-MTBD when SNR = 6 dB and 3 targets
图 14 当SNR = 6dB, 5个目标时, CRPF数量对CRPFB-MTBD性能的影响
Fig. 14 Impact of the number of CRPFs on CRPFB-MTBD when SNR = 6 dB and 5 targets
图 15 当SNR = 6 dB, 3个目标时, 门限$ V_{2}$对CRPFB-MTBD性能的影响
Fig. 15 Impact of threshold $ V_{2}$ on CRPFB-MTBD when SNR = 6 dB and 3 targets
图 16 当SNR = 6 dB, 5个目标时, 门限$ V_{2}$对CRPFB-MTBD性能的影响
Fig. 16 Impact of threshold $ V_{2}$ on CRPFB-MTBD when SNR = 6 dB and 5 targets
表 1 第$l$个目标的先验信息
Table 1 Initial information for the $l {\rm th}$ target
先验信息 $x $方向 $y $方向 初始位置 $x_{l,1}=x_{m_{s}}$ $y_{l,1}=y_{m_{s}}$ 速度范围 $\dfrac{-x_{m_{s}}}{K\triangle T}\leq \dot{x}_{l}\leq \dfrac{N\triangle_{x}-x_{m_{s}}}{K\triangle T}$ $\dfrac{-y_{m_{s}}}{K\triangle T}\leq \dot{y}_{l}\leq \dfrac{M\triangle_{y}-y_{m_{s}}}{K\triangle{T}}$ $k$时刻位置范围 $x_{m_{s}}-(k-1)\dfrac{-x_{m_{s}}}{K\triangle T} \leq x_{m_{s}}+(k-1)\dfrac{N\triangle_{x}-x_{m_{s}}}{K\triangle T}$ $y_{m_{s}}-(k-1)\dfrac{-y_{m_{s}}}{K\triangle T} \leq y_{m_{s}}+(k-1)\dfrac{M\triangle_{y}-y_{m_{s}}}{K\triangle T}$ 
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表 2 4种算法的平均单次运行时间 (s)
Table 2 Average single running time of 4 algorithms (s)
算法名称 运行时间 PHD-TBD 506.8180 PF-MTBD 131.0574 Bernoulli-TBD 6.6079 CRPFB-MTBD 0.0116
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