Acta Photonica Sinica, Volume. 51, Issue 9, 0910003(2022)
Fast Hash_LBP Moving Target Detection Algorithm Based on Hamming Distance Constraint in Complex Background
Figures & Tables(17)
Fig. 6. Schematic of adaptive threshold suppression dynamic background
Fig. 8. Processing results of different algorithms in complex visible light scenes
Fig. 9. Comparative experimental results of two algorithms in infrared scene
Fig. 10. Comparison of Re，Pr and F histograms of six algorithms in each scenario
Table 1. False detection rate of each algorithm in different visible scene
s View tableView in ArticleTable 1. False detection rate of each algorithm in different visible scene
s Algorithm Cano Overpass Highway Skating Blizzard Snowfall CODE_BOOK 0.034 8 0.055 6 0.037 1 0.068 5 0.012 7 0.012 5 LBP_MRF 0.185 5 0.100 5 0.036 1 0.081 3 0.013 3 0.024 4 ViBe 0.051 7 0.038 7 0.039 0 0.048 6 0.010 9 0.011 9 KDE 0.170 8 0.134 0 0.130 9 0.033 4 0.016 7 0.024 2 GMM 0.062 3 0.039 5 0.073 1 0.049 3 0.020 7 0.056 9 Proposed 0.022 5 0.030 8 0.004 7 0.018 4 0.010 4 0.046 5
Table 2. False detection rate of each algorithm in different infrared scenes
View tableView in ArticleTable 2. False detection rate of each algorithm in different infrared scenes
Algorithm Corridor Diningroom Lakeside Library Park CODE_BOOK 0.099 8 0.039 7 0.016 5 0.038 9 0.018 3 LBP_MRF 0.024 0 0.042 7 0.018 4 0.165 9 0.019 2 ViBe 0.024 1 0.063 3 0.021 0 0.202 2 0.017 9 KDE 0.024 5 0.065 0 0.019 0 0.171 0 0.018 0 GMM 0.025 8 0.060 3 0.021 2 0.198 3 0.015 7 Proposed 0.028 6 0.024 3 0.009 4 0.041 0 0.015 1
Table 3. Values of Re、Pr、F and PPWC of different algorithms in each scenario
View tableView in ArticleTable 3. Values of Re、Pr、F and PPWC of different algorithms in each scenario
Dataset Algorithm Re Pr F PPWC Dataset Algorithm Re Pr F PPWC Cano CODE_BOOK 0.737 6 0.665 5 0.699 7 0.034 8 Blizzard CODE_BOOK 0.467 5 0.760 4 0.579 1 0.012 7 LBP_MRF 0.978 9 0.102 4 0.472 9 0.185 5 LBP_MRF 0.657 4 0.680 7 0.668 8 0.013 3 ViBe 0.428 3 0.477 0 0.538 2 0.051 7 ViBe 0.587 6 0.826 5 0.686 9 0.010 9 KDE 0.984 0 0.178 1 0.301 6 0.170 8 KDE 0.216 8 0.857 0 0.346 1 0.016 7 GMM 0.146 2 0.153 0 0.149 5 0.062 3 GMM 0.042 3 0.426 1 0.076 9 0.020 7 Proposed 0.801 0 0.791 8 0.796 4 0.022 5 Proposed 0.690 5 0.692 1 0.691 3 0.010 4 Lakeside CODE_BOOK 0.352 4 0.689 5 0.466 4 0.016 5 Overpass CODE_BOOK 0.783 5 0.403 4 0.532 6 0.055 6 LBP_MRF 0.326 2 0.539 4 0.406 6 0.018 4 LBP_MRF 0.972 4 0.345 5 0.509 9 0.100 5 ViBe 0.124 6 0.621 7 0.207 7 0.021 0 ViBe 0.589 6 0.656 3 0.621 0 0.038 7 KDE 0.057 4 0.569 9 0.104 3 0.019 0 KDE 0.974 2 0.283 2 0.438 9 0.134 0 GMM 0.205 2 0.560 0 0.300 3 0.021 2 GMM 0.602 7 0.641 2 0.621 4 0.039 5 Proposed 0.993 2 0.635 0 0.718 2 0.009 4 Proposed 0.736 0 0.770 7 0.682 3 0.030 8 Snowfall CODE_BOOK 0.688 9 0.798 3 0.739 6 0.012 5 Library CODE_BOOK 0.936 0 0.891 1 0.903 7 0.038 9 LBP_MRF 0.898 3 0.456 8 0.605 6 0.024 4 LBP_MRF 0.333 7 0.806 8 0.472 1 0.165 9 ViBe 0.567 6 0.803 9 0.665 4 0.011 9 ViBe 0.118 8 0.808 4 0.207 1 0.202 2 KDE 0.311 5 0.397 1 0.349 2 0.024 2 KDE 0.277 2 0.857 4 0.419 0 0.171 0 GMM 0.134 8 0.067 5 0.089 9 0.056 9 GMM 0.154 0 0.256 8 0.771 7 0.198 3 Proposed 0.910 3 0.813 3 0.859 1 0.046 5 Proposed 0.899 9 0.914 5 0.907 2 0.041 0 Highway CODE_BOOK 0.774 9 0.657 8 0.711 5 0.037 1 Corridor CODE_BOOK 0.910 6 0.248 6 0.390 2 0.099 8 LBP_MRF 0.905 9 0.627 5 0.758 6 0.036 1 LBP_MRF 0.769 0 0.628 5 0.691 7 0.024 0 ViBe 0.628 6 0.685 5 0.655 8 0.039 0 ViBe 0.458 4 0.741 9 0.566 6 0.024 1 KDE 0.927 8 0.298 8 0.452 0 0.130 9 KDE 0.367 5 0.821 3 0.507 8 0.024 5 GMM 0.498 7 0.403 8 0.446 3 0.073 1 GMM 0.622 3 0.626 8 0.624 6 0.025 8 Proposed 0.939 2 0.697 1 0.658 1 0.004 7 Proposed 0.723 6 0.989 2 0.813 4 0.028 6 Park CODE_BOOK 0.508 6 0.635 7 0.565 1 0.018 3 Skating CODE_BOOK 0.624 0 0.641 3 0.632 5 0.068 5 LBP_MRF 0.877 8 0.453 3 0.597 9 0.019 2 LBP_MRF 0.930 7 0.540 5 0.683 9 0.081 3 ViBe 0.560 7 0.632 8 0.594 6 0.017 9 ViBe 0.579 0 0.816 0 0.677 4 0.048 6 KDE 0.374 3 0.720 0 0.492 5 0.018 0 KDE 0.838 0 0.814 0 0.825 8 0.033 4 GMM 0.567 8 0.703 1 0.628 3 0.015 7 GMM 0.626 5 0.770 4 0.691 0 0.049 3 Proposed 0.607 7 0.603 7 0.605 7 0.015 1 Proposed 0.829 5 0.930 1 0.876 9 0.018 4 Dining
room
CODE_BOOK 0.829 2 0.736 7 0.780 2 0.039 7 LBP_MRF 0.680 3 0.784 2 0.728 6 0.042 7 ViBe 0.288 7 0.878 9 0.434 6 0.063 3 KDE 0.447 0 0.671 6 0.536 8 0.065 0 GMM 0.370 7 0.810 3 0.508 6 0.060 3 Proposed 0.921 4 0.827 0 0.894 9 0.024 3
Table 4. Average processing time of different algorithms
View tableView in ArticleTable 4. Average processing time of different algorithms
Scenes Size Time/ms CODE_BO OK LBP_MRF Vibe KDE GMM Proposed Canoe 320×240 10.38 63.50 8.46 54.60 11.77 10.69 Overpass 320×240 10.31 20.70 8.41 51.35 8.88 15.47 Highway 320×240 10.41 53.33 8.10 46.43 8.79 13.61 Skating 540×360 12.31 94.28 12.18 52.43 11.37 20.11 Blizzard 720×480 14.86 193.62 16.61 52.54 15.81 31.47 Snowfall 720×480 15.63 229.10 19.36 55.63 15.33 31.54 Corridor 320×240 9.31 55.36 6.63 48.67 7.71 11.99 Diningroom 320×240 9.90 52.80 6.87 46.70 10.48 11.82 Lakeside 320×240 7.44 46.84 7.26 48.60 7.84 11.75 Library 320×240 7.02 52.50 12.29 46.84 15.78 11.77 Park 320×240 10.32 98.85 17.74 48.47 16.94 23.52
Table 5. Comparison between CPU usage and memory
View tableView in ArticleTable 5. Comparison between CPU usage and memory
Algorithms
CPU usage
Memory/MB
ViBe
22%
216
GMM
29%
224
CODE_BOOK
24%
229
Proposed
22%
221
Table 6. Comparison results under different algorithms
View tableView in ArticleTable 6. Comparison results under different algorithms
Dataset Cano Algorithm Re Pr F PPWC ViBe 0.428 3 0.477 0 0.538 2 0.051 7 New_ViBe 0.488 0 0.621 7 0.546 8 0.044 2 GMM 0.146 2 0.153 0 0.149 5 0.062 3 New_GMM 0.608 0 0.758 3 0.674 9 0.034 6
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Liya QIU, Weilin CHEN, Fanming LI, Shijian LIU, Xiaoyu WANG, Linhan LI. Fast Hash_LBP Moving Target Detection Algorithm Based on Hamming Distance Constraint in Complex Background[J]. Acta Photonica Sinica, 2022, 51(9): 0910003
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Received: Jan. 8, 2022
Accepted: Feb. 24, 2022
Published Online: Oct. 26, 2022
The Author Email: QIU Liya (qiuliyad@163.com)
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