Abstract
Background: Vision-based dynamic target detection is an important research topic in computer vision, which is the basis for intelligent behavior analysis and event detection. Further research on dynamic target detection methods can help improve target detection and tracking mechanisms while also driving the development of other related fields. Hence, conducting a review on vision-based dynamic target detection is very significant.
Objective: There are many methods for dynamic target detection. This paper introduces their classification, characteristics, advantages, disadvantages and development trends.
Methods: This paper reviews recent patents and representative articles on dynamic target detection in simple visual and complex contexts. The crucial methods of these references are introduced from the aspects of algorithm, innovation, and principle.
Results: This paper analyzes and compares the existing dynamic target detection methods, summarizes their characteristics, main applications, and advantages and disadvantages in the current development stage, and discusses the future development and potential problems of dynamic target tracking methods.
Conclusion: Vision-based dynamic target detection can accurately extract moving targets from the scene. Due to its inherent complexity, each detection method has its advantages and disadvantages in specific scenes. Currently, the research mainly focuses on the real-time robustness and accuracy of the algorithm, which needs to be further improved in the aspects of algorithm innovation, multialgorithm fusion, multi-target recognition, and algorithm adaptability. Therefore, relevant research patents and documents should be put forward, initiating the future development trend.
Graphical Abstract
[1]
J. Sun, F. He, Y. Chen, and X. Chen, "A multiple template approach for robust tracking of fast motion target", Appl. Math. J. Chin. Univ., vol. 31, no. 2, pp. 177-197, 2016.
[http://dx.doi.org/10.1007/s11766-016-3378-z]
[http://dx.doi.org/10.1007/s11766-016-3378-z]
[3]
D. Zhao, Y. Liu, and C. Zhang, "Autonomous driving simulation for unmanned vehicles", Work-Shop Appl. Comput. Vision, pp. 185-190, 2015.
[4]
S. Youm, and S. Liu, "Development healthcare PC and multimedia software for improvement of health status and exercise habits", Multimedia Tools Appl., vol. 76, no. 17, pp. 17751-17763, 2017.
[http://dx.doi.org/10.1007/s11042-015-2998-2]
[http://dx.doi.org/10.1007/s11042-015-2998-2]
[5]
J. Zhang, F.Y. Wang, K. Wang, W-H. Lin, X. Xu, and C. Chen, "Data-driven intelligent transportation systems: A survey", IEEE Trans. Intell. Transp. Syst., vol. 12, no. 4, pp. 1624-1639, 2011.
[http://dx.doi.org/10.1109/TITS.2011.2158001]
[http://dx.doi.org/10.1109/TITS.2011.2158001]
[6]
Y. Zhao, H. Shi, and X. Chen, "An overview of object detection and tracking", Inter. Conf. Inform. Autom., pp. 280-286, 2015.
[7]
A. Sanin, C. Sanderson, and B.C. Lovell, "Shadow detection: A survey and comparative evaluation of recent methods", Pattern Recognit., vol. 45, no. 4, pp. 1684-1695, 2012.
[http://dx.doi.org/10.1016/j.patcog.2011.10.001]
[http://dx.doi.org/10.1016/j.patcog.2011.10.001]
[8]
A. Prati, I. Mikic, M.M. Trivedi, and R. Cucchiara, "Detecting moving shadows: algorithms and evaluation", IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 7, pp. 918-923, 2003.
[http://dx.doi.org/10.1109/TPAMI.2003.1206520]
[http://dx.doi.org/10.1109/TPAMI.2003.1206520]
[9]
G. Tu, H. Karstoft, L. Pedersen, and E. Jørgensen, "Illumination and reflectance estimation with its application in foreground detection", Sensors (Basel), vol. 15, no. 9, pp. 21407-21426, 2015.
[http://dx.doi.org/10.3390/s150921407] [PMID: 26343675]
[http://dx.doi.org/10.3390/s150921407] [PMID: 26343675]
[10]
W. Hu, T. Tan, and L. Wang, "A survey on visual surveillance of object motion and behaviors", Syst. Man Cyber., vol. 34, no. 3, pp. 334-352, 2004.
[11]
L. Gang, N. Shangkun, and Y. Yugan, "An improved moving objects detection algorithm", Inter. Conf. Wavelet Anal. Patt. Recogn., pp. 96-102, 2013.
[12]
K. Yano, I. Umeda, M. Guo, and L.F. Xu, "Method and apparatus for detecting object in moving image and storage medium storing program thereof", US20160260226A1, 2015.
[13]
G. Shi, J. Suo, and C. Liu, "Moving target detection algorithm in image sequences based on edge detection and frame difference", In IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC)., IEEE, 2017, pp. 740-744
[14]
J.W. Hsieh, "Fast stitching algorithm for moving object detection and mosaic construction", Image Vis. Comput., vol. 22, no. 4, pp. 291-306, 2004.
[http://dx.doi.org/10.1016/j.imavis.2003.09.018]
[http://dx.doi.org/10.1016/j.imavis.2003.09.018]
[15]
P. Tissainayagam, and D. Suter, "Object tracking in image sequences using point features", Pattern Recognit., vol. 38, no. 1, pp. 105-113, 2005.
[http://dx.doi.org/10.1016/j.patcog.2004.05.011]
[http://dx.doi.org/10.1016/j.patcog.2004.05.011]
[16]
J. Yin, L. Liu, H. Li, and Q. Liu, "The infrared moving object detection and security detection related algorithms based on W4 and frame difference", Infrared Phys. Technol., vol. 77, pp. 302-315, 2016.
[http://dx.doi.org/10.1016/j.infrared.2016.06.004]
[http://dx.doi.org/10.1016/j.infrared.2016.06.004]
[17]
S. Okumura, S. Otani, and F. Igarashi, "Person detecting device and person detecting method", US10026019B2, 2014.
[18]
M. Piccardi, "Background subtraction techniques: A review", Syst., Man Cybern, pp. 3099-3104, 2004.
[19]
P. Ramya, and R. Rajeswari, "A modified frame difference method using correlation coefficient for background subtraction", Procedia Comput. Sci., vol. 93, pp. 478-485, 2016.
[http://dx.doi.org/10.1016/j.procs.2016.07.236]
[http://dx.doi.org/10.1016/j.procs.2016.07.236]
[20]
S.H. Cheng, X. Gao, S.C. Cheng, and H.J. Lu, "Moving vehicle detection method based on background updating and particle swarm optimization al-gorithm", CN105069441(A), 2018.
[21]
W. Huang, Y. Kang, and S. Zheng, "An improved frame difference method for moving target detection", Chin. Auto. Congr., vol. 11, no. 3, pp. 1537-1541, 2017.
[22]
B.B. Xiao, and W. Hu, "Foreground extraction in surveillance scene", Comput. Eng. Des., vol. 37, no. 3, pp. 695-699, 2016.
[23]
S. Fang, and X. Wei, "A forest smoke detection method based on background differential and block matching alignment", CN111243216(A), 2011.
[24]
Y. Xue, L. Wang, Y.F. Zhang, S. Wang, N. Zhang, and Z.C. Yu, "Moving object detection algorithm in jitter video sequence", CN109166137, 2014.
[25]
Y. Benezeth, P. Jodoin, and B. Emile, "Review and evaluation of commonly-implemented back-ground subtraction algorithms", In. 19th International Conference On Pattern Recognition, 08-11 December
2008, Tampa, FL, USA, IEEE, 2008.
[26]
O. Barnich, and M. Van Droogenbroeck, "ViBE: A powerful random technique to estimate the background in video sequences", In. International Conference On Acoustics, Speech, And Signal Processing,
19-24 April 2009, Taipei, Taiwan, IEEE, pp. 945-948, 2009.
[27]
O. Barnich, and M. Van Droogenbroeck, "ViBe: a universal background subtraction algorithm for video sequences", IEEE Trans. Image Process., vol. 20, no. 6, pp. 1709-1724, 2011.
[http://dx.doi.org/10.1109/TIP.2010.2101613] [PMID: 21189241]
[http://dx.doi.org/10.1109/TIP.2010.2101613] [PMID: 21189241]
[28]
M. Wei-dong, G. Xiao-guang, and HAN. Qing, "An improved ViBe algorithm and its application in traffic video processing", Optics Precision Eng., vol. 25, no. 3, pp. 806-811, 2017.
[http://dx.doi.org/10.3788/OPE.20172503.0806]
[http://dx.doi.org/10.3788/OPE.20172503.0806]
[29]
Y. Luo, H. Zhou, Q. Tan, X. Chen, and M. Yun, "Key frame extraction of surveillance video based on moving object detection and image similarity", Pattern Recognit. Image Anal., vol. 28, no. 2, pp. 225-231, 2018.
[http://dx.doi.org/10.1134/S1054661818020190]
[http://dx.doi.org/10.1134/S1054661818020190]
[30]
K. Cheng, K. Hui, and Y. Zhan, "A novel improved ViBe algorithm to accelerate the ghost suppression", In. 12th International Conference
on Natural Computation, Fuzzy Systems and Knowledge Discovery
(ICNC-FSKD), 13-15 August 2016, Changsha, China, IEEE, pp. 1692-1698, 2016.
[32]
W. Huang, L. Liu, C. Yue, and H. Li, "The moving target detection algorithm based on the improved visual background extraction", Infrared Phys. Technol., vol. 71, pp. 518-525, 2015.
[http://dx.doi.org/10.1016/j.infrared.2015.06.011]
[http://dx.doi.org/10.1016/j.infrared.2015.06.011]
[33]
JH Zhu, F Zhang, and LG Yu, "ViBe ghost suppression method fusing inter-frame difference method", CN110599523(A).
[34]
P.X. Cui, and F.Q. Yu, "Improved Vibe moving target detection algorithm to eliminate ghosts and shadows", Adv. Laser Optoelectr., pp. 1-15, 2020.
[35]
Y. Zhang, X. Zhao, and M. Tan, "Motion detection based on improved Sobel and ViBe algorithm[C]", In Chinese Control Conference, CCC 2016, Chengdu, China, 2016, pp. 4143-4148
[36]
S Zhang, XD Wang, and CH Wang, "Vibe dynamic target detection method based on depth sensor", CN109671047(A).
[37]
B. Joakim, "Method and apparatus for updating a background model used for background sub-traction of an image", US10152645B2, 2021.
[38]
Y. Li, K. Cao, and J. Wang, "An improved algorithm for ghost suppression based on ViBe", J. Guangxi Univ., vol. 42, no. 2, pp. 712-719, 2017.
[39]
C. Stauffer, and W.E. Grimson, "Adaptive background mixture models for real-time tracking", Comput. Vision Pattern Recogn, pp. 246-252, 1999.
[40]
A. Elgammal, D. Harwood, and L.S. Davis, "Non-parametric model for background subtraction", In: Vernon D., Ed., Computer Vision-ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol. 1843. Springer: Berlin, Heidelberg, 2000, pp. 751-767.
[41]
H. Li, A. Achim, and D. Bull, "GMM-based efficient foreground detection with adaptive region update", In. 16th IEEE International Conference on Image Processing (ICIP), 07-10 November 2009,
Cairo, Egypt, IEEE, pp. 3145-3148, 2009.
[42]
Y. Wang, P. Jodoin, and F. Porikli, "CDnet 2014: An expanded change detection benchmark dataset", In. 2014 IEEE Conference
on Computer Vision and Pattern Recognition Workshops, 23-28
June 2014, Columbus, OH, USA, IEEE, pp. 393-400, 2014.
[43]
P. KaewTraKulPong, and R. Bowden, "An improved adaptive background mixture model for real-time tracking with shadow detection", In: Remagnino, P. and Jones, G.A., Eds., Computer Vision and Distributed Processing., Springer, New York, 2002, pp. 135-144.
[44]
Z. Zivkovic, and F. van der Heijden, "Efficient adaptive density estimation per image pixel for the task of background subtraction", Pattern Recognit. Lett., vol. 27, no. 7, pp. 773-780, 2006.
[http://dx.doi.org/10.1016/j.patrec.2005.11.005]
[http://dx.doi.org/10.1016/j.patrec.2005.11.005]
[45]
B. Tu, Y.Y. Liu, B.S. Liu, J. Yang, Q.L. Shang, and M.Z. Li, "Laser target detection method and device based on background modeling and background difference", CN108629792, 2018.
[46]
J. Choi, H.J. Chang, J.Y. Yoo, and J.Y. Choi, "Robust moving object detection against fast illumination change", Comput. Vis. Image Underst., vol. 116, no. 2, pp. 179-193, 2012.
[47]
X.Y. Chen, X.F. Lv, and J. Liu, "An improved algorithm for real-time moving target detection based on gaussian mixture model", Appl. Mech. Mater., vol. 599-601, pp. 814-818, 2014.
[http://dx.doi.org/10.4028/www.scientific.net/AMM.599-601.814]
[http://dx.doi.org/10.4028/www.scientific.net/AMM.599-601.814]
[48]
HB Ji, YP Xu, WB Zhang, GP Huang, and ZG Zhu, "Motion target detection method based on improved mixed Gaussian model", CN107292905, 2017.
[49]
A. Boulmerka, and M.S. Allili, "Foreground segmentation in videos combining general gaussian mixture modeling and spatial information", IEEE Trans. Circ. Syst. Video Tech., vol. 28, no. 6, pp. 1330-1345, 2018.
[http://dx.doi.org/10.1109/TCSVT.2017.2665970]
[http://dx.doi.org/10.1109/TCSVT.2017.2665970]
[50]
E. Dong, B. Han, and H. Jian, "Moving target detection based on improved Gaussian mixture model considering camera motion", Multimedia Tools Appl., pp. 1-16, 2019.
[51]
H. Greenspan, J. Goldberger, and A. Mayer, "Probabilistic space-time video modeling via piecewise gmm", IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 3, pp. 384-396, 2004.
[http://dx.doi.org/10.1109/TPAMI.2004.1262334] [PMID: 15376884]
[http://dx.doi.org/10.1109/TPAMI.2004.1262334] [PMID: 15376884]
[52]
M. Kristan, J. Perš, S. Kovačič, and A. Leonardis, "A local-motion-based probabilistic model for visual tracking", Pattern Recognit., vol. 42, no. 9, pp. 2160-2168, 2009.
[http://dx.doi.org/10.1016/j.patcog.2009.01.002]
[http://dx.doi.org/10.1016/j.patcog.2009.01.002]
[53]
L Ma, H Zhao, CG He, and XZ Tan, "Indoor positioning method based on gray feature extraction and dense optical flow method", CN106482711, 2013.
[54]
Y. Benabbas, N. Ihaddadene, and C. Djeraba, "Motion pattern extraction and event detection for automatic visual surveillance", EURASIP J. Image Video Process., vol. 2011, no. 1, pp. 1-15, 2011.
[http://dx.doi.org/10.1155/2011/163682]
[http://dx.doi.org/10.1155/2011/163682]
[55]
W. Jing, W. Bing, and W. Ke, "Design and implementation of face recognition system in security check area of railway passenger station", Rail. Comput. Appl, no. 06, pp. 83-86, 2012.
[56]
Y. Peng, Z. Chen, Q.M.J. Wu, and C. Liu, "Traffic flow detection and statistics via improved optical flow and connected region analysis", Signal Image Video Process., vol. 12, no. 1, pp. 99-105, 2018.
[http://dx.doi.org/10.1007/s11760-017-1135-2]
[http://dx.doi.org/10.1007/s11760-017-1135-2]
[57]
S. Shigemura, "Moving object detection apparatus and moving object detection method", US10192106B2, 2019.
[58]
E. Meinhardtllopis, J.S. Perez, and D. Kondermann, "Horn-schunck optical flow with a multi-scale strategy", Image Process. Line, pp. 151-172, 2013.
[59]
F. Steinbrucker, T. Pock, and D. Cremers, "Large displacement optical flow computation without warping", In: ICCV, pp. 1609-1614, 2009.
[60]
H.W. Haussecker, and D.J. Fleet, "Computing optical flow with physical models of brightness variation", IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 661-673, 2001.
[http://dx.doi.org/10.1109/34.927465]
[http://dx.doi.org/10.1109/34.927465]
[61]
A. Bruhn, J. Weickert, T. Kohlberger, and C. Schnörr, "A multigrid platform for real-time motion computation with discontinuity-preserving variational methods", Int. J. Comput. Vis., vol. 70, no. 3, pp. 257-277, 2006.
[http://dx.doi.org/10.1007/s11263-006-6616-7]
[http://dx.doi.org/10.1007/s11263-006-6616-7]
[62]
S. Denman, V. Chandran, and S. Sridharan, "An adaptive optical flow technique for person tracking systems", Pattern Recognit. Lett., vol. 28, no. 10, pp. 1232-1239, 2007.
[http://dx.doi.org/10.1016/j.patrec.2007.02.008]
[http://dx.doi.org/10.1016/j.patrec.2007.02.008]
[63]
Li Ying, YW Wang, and JG Li, "Unmanned aerial vehicle aerial video moving target detection method based on time-space-frequency significance", CN109767454, 2018.
[64]
R. Hu, and J. Tian, "Research on moving object detection algorithm based on optical flow method", Electron. World, no. 05, pp. 58-61, 2019.
[65]
W. Liu, D. Anguelov, and D. Erhan, "SSD: Single shot multibox detector", In: European Conference on Computer Vision, Springer: Cham, 2016, pp. 21-37.
[66]
W. Shuai, X. Yong, and D. Zhao, "A survey of target detection based on deep convolution network", Pattern Recogn. Artif. Intell., vol. 31, no. 4, pp. 335-346, 2018.
[67]
W. Ying, and Y. Sai, "Review of bottom-up salient target detection", Recent Pat. Comput. Sci., vol. 46, no. 3, pp. 48-52, 2019.
[68]
Z.Q. Zhao, P. Zheng, S.T. Xu, and X. Wu, "Object detection with deep learning: A review", IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 11, pp. 3212-3232, 2019.
[http://dx.doi.org/10.1109/TNNLS.2018.2876865] [PMID: 30703038]
[http://dx.doi.org/10.1109/TNNLS.2018.2876865] [PMID: 30703038]
[69]
J. Redmon, S. Divvala, and R. Girshick, "You only look once: Unified, real-time object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
[70]
J. Redmon, and A. Farhadi, "YOLO9000: Better, faster, stronger", "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition", . Honolulu, 21-26 July 2017, 7263-7271, 2017.
[71]
S. Ioffe, and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift", In ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, July 2015, 2015, pp. 448-456
[72]
J Redmon, and A. Farhadi, ""Yolov3: An incremental improvement",
CoRR, 2018", Available from: https://arxiv.org/abs/1804.02767
Related Books
Advanced Characterization Technologies for Secondary Batteries
Design and Construction of Buildings and Foundations with Illustrative Examples
Robotics and Automation in Industry 4.0
Biomedical Applications of Perovskites: The Era of Bio-Piezoelectric Systems
Smart Kinematics For Modern Engineering Students
Computational Modeling and Simulation in Biomedical Research
Introduction to Advanced Soft Robotics
Intelligent Technologies for Research and Engineering
The Production of Biodiesel and Related Fuel Additives
Intelligent Technologies for Research and Engineering
