The motion shadow is conglutinous with the object, and has the consistency of motion. It is often misdetected as a part of the moving target. The existence of motion shadow changes the shape of the moving object and influences the further analysis of the foreground of the moving target. To solve this problem, a motion shadow removal algorithm based on improved firefly optimization algorithm is proposed. The optimal threshold is obtained by optimizing the 2-Otsu distance measure function based on the improved glowworm algorithm which is based on the influence of the best position in the population history, and then the image is segmented and the moving shadow is removed. Compared our method with the traditional 2-Otsu method, particle swarm optimization (PSO) optimize 2-Otsu method, firefly optimization algorithm (FA) optimize 2-Otsu method, the experimental results show that the algorithm are 2.69, 1.42 and 1.21 times faster than the other three methods in the presence of shadow. Besides, it is superior to the other three algorithms in terms of region consistency, shadow detection rate and recognition rate. The effectiveness of the method is verified.
Application of improved firefly optimization algorithm in motion shadow removal
First published at:Apr 01, 2019
1 Zheng L X, Ruan X Y, Chen Y B, et al. Shadow removal for pedestrian detection and tracking in indoor environments[J]. Multimedia Tools and Applications, 2017, 76(18): 18321-18337. DOI:10.1007/s11042-016-3880-6
2 Yang W D, Guo W, Peng K, et al. Research on removing shadow in workpiece image based on homomorphic filtering[J]. Procedia Engineering, 2012, 29: 2360-2364. DOI:10.1016/j.proeng.2012.01.315
3 Vicente T F Y, Hoai M, Samaras D. Leave-one-out kernel optimization for shadow detection and removal[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 682-695. DOI:10.1109/TPAMI.2017.2691703
4 Zhou Y H, Sun L, Zhang J B. A shadow elimination method based on color and texture[C]//Proceedings of 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, Xiamen, China, 2010: 8-11.
5 Jia Y, Yu X, Dai J, et al. A novel moving cast shadow detection of vehicles in traffic scene[M]//Yang J, Fang F, Sun C Y. Intelligent Science and Intelligent Data Engineering. Berlin, Heidelberg: Springer, 2013: 115-124.
6 Leone A, Distante C. Shadow detection for moving objects based on texture analysis[J]. Pattern Recognition, 2007, 40(4): 1222-1233. DOI:10.1016/j.patcog.2006.09.017
7 Cao J, Chen H Q, Zhang K, et al. Moving cast shadow detec -tion based on region color and texture[J]. Robot, 2011, 33(5): 628-633. DOI:10.3724/SP.J.1218.2011.00628
曹健, 陈红倩, 张凯, 等.结合区域颜色和纹理的运动阴影检测方法[J].机器人, 2011, 33(5): 628-633. DOI:10.3724/SP.J.1218.2011.00628
8 Al-Najdawi N. Cast shadow modelling and detection[J]. Loughborough University, 2006. Ai-Najdawi N, Bez H E, Edirisinghe E A. A Novel Approach For Cast Shadow Modelling and Detection[C]//Iet International Conference on Visual Information Engineering. IET, 2007: 553-558.
9 Sanin A, Sanderson C, Lovell B C. Improved shadow removal for robust person tracking in surveillance scenarios[C]//Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010: 141-144.
10 Hu X B, Huang X Y. Solving 0-1 knapsack problem based on ant colony optimization algorithm[J]. Journal of Systems Engineering, 2005, 20(5): 520-523.
11 Zhu L J, Yuan W Q. An eyelash extraction method based on improved ant colony algorithm[J]. Opto-Electronic Engineering, 2016, 43(6): 44-50. DOI:10.3969/j.issn.1003-501X.2016.06.008
朱立军, 苑玮琦.一种改进蚁群算法的睫毛提取[J].光电工程, 2016, 43(6): 44-50. DOI:10.3969/j.issn.1003-501X.2016.06.008
12 Liu G H, Zhao L, Sun J G, et al. An Otsu image threshold segmentation method based on improved particle swarm optimization[J]. Computer Science, 2016, 43(3): 309-312. DOI:10.3969/j.issn.1007-0249.2003.05.020
刘桂红, 赵亮, 孙劲光, 等.一种改进粒子群优化算法的Otsu图像阈值分割方法[J].计算机科学, 2016, 43(3): 309-312. DOI:10.3969/j.issn.1007-0249.2003.05.020
13 Li G S, Chou W S. Path planning for mobile robot using self-adaptive learning particle swarm optimization[J]. Science China Information Sciences, 2018, 61(5): 052204. DOI:10.1007/s11432-016-9115-2
14 Liu Z G, Ji X H, Liu Y X. Hybrid non-parametric particle swarm optimization and its stability analysis[J]. Expert Systems with Applications, 2017, 92: 256-275. DOI:10.1016/j.asoc.2014.12.015
15 Xu C P, Cai Z, Hu C. On-line test path optimization for digital microfluidic biochips based on ant colony algorithm[J]. Chinese Journal of Scientific Instrument, 2014, 35(6): 1417-1424.
16 Zhou C H, Tian L W, Zhao H W, et al. A method of two-dimensional Otsu image threshold segmentation based on improved firefly algorithm[C]//Proceedings of 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, Shenyang, China, 2015: 1420-1424.
17 Huang L, Fang Y M, Zuo X Q, et al. Automatic change detection method of multitemporal remote sensing images based on 2D-otsu algorithm improved by firefly algorithm[J]. Journal of Sensors, 2015, 2015: 327123.
18 Lieu Q X, Do D T T, Lee J. An adaptive hybrid evolutionary firefly algorithm for shape and size optimization of truss structures with frequency constraints[J]. Computers & Structures, 2018, 195: 99-112.
19 Liu J, Jin W D. Fast thresholding algorithm of 2D Otsu for low SNR image[J]. Application Research of Computers, 2013, 30(10): 3169-3171, 3200. DOI:10.3969/j.issn.1001-3695.2013.10.072
刘金, 金炜东.噪声图像的快速二维Otsu阈值分割[J].计算机应用研究, 2013, 30(10): 3169-3171, 3200. DOI:10.3969/j.issn.1001-3695.2013.10.072
20 Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987. DOI:10.1109/TPAMI.2002.1017623
National Major Instrument and Equipment Development Special (2016YFF0101402) and Shanghai University Young Teachers Training Program Funded Projects (ZZsl15008)
Get Citation: Liu Lei, Cao Min, Zhang Xiao. Application of improved firefly optimization algorithm in motion shadow removal[J]. Opto-Electronic Engineering, 2019, 46(4): 180120.