UAV target tracking based on visual attention mechanism

doi:10.3969/j.issn.0253-2778.2020.08.017

Abstract

Abstract: In recent years, the demand for small Unmanned Aerial Vehicles (UAV) in GPS-denied environment is increasingly strong. To solve the problem of multi-target recognition, we study the multi moving target recognition and location technology based on the platform of the small multi-rotor UAV. We used a method to quickly locate the region of interest based on the visual attention mechanism, and then used the machine learning algorithm to classify the region of interest to obtain the target accurately. Our method can track the specified target in the image and locate the target in real time, which the algorithm delay is about 50ms and the location error is less than 15 cm. Our solution can effectively reduce the influence of light variation, motion blur, the color analogue interference and complex background. The ground robot is used as the tracking target to test and verify the algorithm, which can achieve a better tracking effect.

Key words: unmanned Aerial Vehicle, target tracking, visual attention mechanism, machine learning

CLC Number:

TP18

References

［1］ FORSYTH D A, PONCE J. Computer Vision: A Modern Approach[M]. Second Edition. New Jersey: Prentice Hall, 2012: 255-261.
[2] COMANICIU D, MEER P. Mean shift: A robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.
[3] NING J F, ZHANG L, ZHANG D, et al. Interactive image Segmentation by maximal similarity based region merging[J]. Pattern Recognition, 2010, 43(2): 445-456.
[4] MEDEIROS F N S, CARVALHO E A, USHIZIMA D M, et al. SAR imagery segmentation by statistical region growing and hierarchical merging[J]. Digital Signal Processing, 2010, 20(5): 1365-1378.
[5] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]// Computer Vision and Pattern Recognition. IEEE, 2010:2544-2550.
[6] UGARRIZA L G, SABER E, VANTARAM S R, et al. Automatic image segmentation by dynamic region growth and multiresolution merging[J]. IEEE Transactions on Image Processing, 2009, 18(10): 2275-2288.
[7] ZHAI W X, CHENG C Q. A camshift motion tracking algorithm based on kalman filter[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2015, 51(5):799-804.
[8] YU W. Object tracking with particle filter in UAV video[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2013, 8918(48):10.
[9] LI Z H, WANG L, CUI J G. Weak aerial target tracking algorithm based on Camshift and Particle Filter[J]. Computer Engineering and Applications, 2011, 47(9):192-195.
[10] ZHAO P, SHEN T, SHAN B. An object tracking algorithm for TV guiding systemof UAV based on particle filter[J]. Optics and Precision Engineering, 2008, 1: 026.
[11] ZHAO P, SHEN T, SHAN B. An object tracking algorithm for TV guiding system of UAV based on particle filter[J]. Optics and Precision Engineering, 2008, 1: 026.
[12] WANG W, MENG Z H. Target tracking base on improved Camshift algorithm[J]. Information Technology, 2015(1):85-88.
[13] CHANG L, DUARTE M M, SUCAR L E, et al. A Bayesian approach for object classification based on clusters of SIFT local features[J]. Expert Systems with Applications, 2012, 39(2): 1679-1686.
[14] YU G, YING X. Architecture design of deep convolutional neural network for SAR target recognition[J]. Journal of Image and Graphics, 2018.
[15] RUSSAKOVSKY O, DENG J. Imagenet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3):211-252.
[16] UIJLINGS J R R, SANDE K E A, GEVERS T. Selective search for object recognition[J]. International Journal of Computer Vision, 2013.
[17] REDMON J, FARHADI A. YOLO9000: Better, Faster, Stronger [C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2017:6517-6525.
[18] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2005, 1: 886-893.
[19] ZHAO B, WU X, FENG J, et al. Diversified visual attention networks for fine-grained object classification[J]. IEEE Transactions on Multimedia, 2017, 19(6):1245-1256.
[20] ITTI L, KOCH C. Computational modelling of visual attention[J]. Nature Reviews Neuroence, 2001, 2(3):194-203.
[21] HOU X, ZHANG L. Saliency detection: A spectral residual approach[C]// IEEE Conference on Computer Vision and Pattern Recognition.IEEE, 2007:11-19.
[22] HEMAMI S, ESTRADA F, SUSSTRUNK S. Frequency-tuned salient region detection[C]// IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009). IEEE, 2009:1597-1604.
[23] GOFERMAN S, ZELNIKMANOR L, TAL A. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(10):1915-26.
[24] LIU Z, LE M, LUO S. Superpixel-based saliency detection[J]. International Workshop on Image Analysis for Multimedia Interactive Services, 2013, 8215(2):1-4.
[25] HAREL J, KOCH C, PERONA P. Graph-based visual saliency[C]. Proceedings of Neural Information Processing Systems (NIPS), 2007.
[26] KLETTE R. Concise Computer Vision-An Introduction into Theory and Algorithms[M]. Springer London, 2014.
[27] Chang C C, Lin C J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems & Technology, 2011, 2(3):389-396.
[28] HOFMANN T, SCHLKOPF B, SMOLA A J. Kernel methods in machine learning[J]. Annals of Statistics, 2008, 36(3):1171-1220.
[29] ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.

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