基于代價(jià)參考粒子濾波器組的多目標檢測前跟蹤算法
doi: 10.16383/j.aas.c220635
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陜西科技大學(xué)電子信息與人工智能學(xué)院 西安 710021 中國
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施耐德(西安)創(chuàng )新技術(shù)有限公司 西安 710121 中國
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格里菲斯大學(xué)集成與智能系統研究所 布里斯班 4111 澳大利亞
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聯(lián)邦科學(xué)與工業(yè)研究組織Data61中心 悉尼 1710 澳大利亞
A Multi-target Track-before-detect Algorithm Based on Cost-reference Particle Filter Bank
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School of Electrical Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi＇an 710021, China
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Schneider (Xi＇an) Innovation & Technology Company Limited, Xi＇an 710121, China
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Institute of Integrated and Intelligent Systems, Griffith University, Brisbane 4111, Australia
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Data61, Commonwealth Scientific and Industrial Research Organization, Sydney 1710, Australia
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摘要: 針對圖像序列中多目標檢測和跟蹤算法結構復雜、計算量大、性能降低等問(wèn)題, 提出一種基于代價(jià)參考粒子濾波器組的多目標檢測前跟蹤(Cost-reference particle filter bank based multi-target track-before-detect, CRPFB-MTBD)算法, 將多目標跟蹤問(wèn)題轉換為序貫地檢測和跟蹤多個(gè)單目標的問(wèn)題. 首先, 采用代價(jià)參考粒子濾波器組序貫地估計所有可能單目標狀態(tài)序列; 其次, 基于所有可能單目標狀態(tài)序列的歐氏距離和累積代價(jià)確定目標數量; 最后, 根據累積代價(jià)判斷每個(gè)目標出現和消失的具體時(shí)刻. 仿真實(shí)驗驗證了CRPFB-MTBD的優(yōu)良性能, 與基于傳統粒子濾波的多目標檢測前跟蹤算法(Particle filter based multi-target track-before-detect, PF-MTBD)、基于概率假設密度的檢測前跟蹤算法(Probability hypothesis density based track-before-detect, PHD-TBD)和基于伯努利濾波的檢測前跟蹤算法(Bernoulli based track-before-detect, Bernoulli-TBD) 相比, CRPFB-MTBD的目標狀態(tài)序列和數量估計結果最佳, 且平均單次運行時(shí)間極短.
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關(guān)鍵詞:
- 多目標跟蹤 /
- 檢測前跟蹤 /
- 粒子濾波 /
- 代價(jià)參考粒子濾波器組 /
- 濾波器組
Abstract: 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 cumulative 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 density 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. -
圖 6 判斷各個(gè)目標存在的具體時(shí)刻
Fig. 6 Determination of the specific moments when each target existing
圖 8 當SNR = 6 dB, 3個(gè)目標時(shí), 4種方法的OSPA
Fig. 8 Comparison of OSPAs resulted from 4 algorithms when SNR = 6 dB and 3 targets
圖 9 當SNR = 8 dB, 3個(gè)目標時(shí), 4種方法的OSPA
Fig. 9 Comparison of OSPAs resulted from 4 algorithms when SNR = 8 dB and 3 targets
圖 10 當SNR = 8 dB, 3個(gè)目標時(shí), CRPFB-MTBD的目標狀態(tài)估計結果
Fig. 10 State estimation of CRPFB-MTBD when SNR = 8 dB and 3 targets
圖 11 當SNR = 8 dB, 3個(gè)目標時(shí), CRPFB-MTBD的目標數量估計結果
Fig. 11 Estimation of target number provided by CRPFB-MTBD when SNR = 8 dB and 3 targets
圖 12 當SNR = 6 dB時(shí), 目標數量對CRPFB-MTBD性能的影響
Fig. 12 Impact of target number on the performance of CRPFB-MTBD when SNR = 6 dB
圖 13 當SNR = 6 dB, 3個(gè)目標時(shí), CRPF數量對CRPFB-MTBD性能的影響
Fig. 13 Impact of the number of CRPFs on the performance of CRPFB-MTBD when SNR = 6 dB and 3 targets
圖 14 當SNR = 6 dB, 5個(gè)目標時(shí), CRPF數量對CRPFB-MTBD性能的影響
Fig. 14 Impact of the number of CRPFs on the performance of CRPFB-MTBD when SNR = 6 dB and 5 targets
圖 15 當SNR = 6 dB, 3個(gè)目標時(shí), 門(mén)限$ V_{2}$對CRPFB-MTBD性能的影響
Fig. 15 Impact of threshold $ V_{2}$ on the performance of CRPFB-MTBD when SNR = 6 dB and 3 targets
圖 16 當SNR = 6 dB, 5個(gè)目標時(shí), 門(mén)限$ V_{2}$對CRPFB-MTBD性能的影響
Fig. 16 Impact of threshold $ V_{2}$ on the performance of CRPFB-MTBD when SNR = 6 dB and 5 targets
表 1 第$l$個(gè)目標的先驗信息
Table 1 Apriori information for the $l\text{-} {\rm th}$ target
先驗信息 $x $方向 $y $方向 初始位置 $x_{l,1}=x_{m_{s}}$ $y_{l,1}=y_{m_{s}}$ 速度范圍 $-\dfrac{x_{m_{s} } }{K\triangle T}\leq \dot{x}_{l}\leq \dfrac{N\triangle_{x}-x_{m_{s} } }{K\triangle T}$ $-\dfrac{y_{m_{s} } }{K\triangle T}\leq \dot{y}_{l}\leq \dfrac{M\triangle_{y}-y_{m_{s} } }{K\triangle{T} }$ $k$時(shí)刻位置范圍 $x_{m_{s}}-(k-1)\dfrac{-x_{m_{s}}}{K\triangle T} \leq x_{m_{s}}+(k-1)\dfrac{N\triangle_{x}-x_{m_{s}}}{K\triangle T}$ $y_{m_{s}}-(k-1)\dfrac{-y_{m_{s}}}{K\triangle T} \leq y_{m_{s}}+(k-1)\dfrac{M\triangle_{y}-y_{m_{s}}}{K\triangle T}$ 
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表 2 4種算法的平均單次運行時(shí)間 (s)
Table 2 Average single running time of 4 algorithms (s)
算法名稱(chēng) 運行時(shí)間 PHD-TBD 506.8180 PF-MTBD 131.0574 Bernoulli-TBD 6.6079 CRPFB-MTBD 0.0116
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