Abstract
Target recognition and tracking technology has become a core technology for UAVs to visually perceive and understand the battlefield environment. Therefore, this paper proposes an algorithm to study UAVs tracking video targets. In the framework proposed, according to the motion model, the particle filter is used to predict the target position at each frame of the image sequence, the input of the CNN are those particles that round the position predicted, and the adaptive correlation filter is learned on the output of each layer of the CNN to encode the target appearance, and then through a correlation filter maintains the long-term memory of the target’s appearance. Finally, the output of the CNN and the correlation filter is used to determine the particle weights, and the target position of the current sequence of the image sequence is calculated based on the particles and their weight. By using the Visual Tracker Benchmark v1.0 to test and evaluate the algorithm, we can find that the algorithm has good tracking performance.
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Son BJ, Kim YJ, Cho S (2013) Method and terminal for detecting and tracking moving object using real-time camera motion estimation. US, US8611595, pp 33–34
Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575
Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072
Ross DA, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. Int. J Comput Vis 77(1–3):125–141
Mei X, Ling H (2009) Robust visual tracking using L1 minimization. In: Proceedings of IEEE international conference on computer vision
Babenko B, Yang M-H, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632
Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422
Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596
Li H, Li Y, Porikli F (2016) DeepTrack: learning discriminative feature representations online for robust visual tracking. IEEE Trans Image Process 25(4):1834–1848
Nam H, Han B (June 2016) Learning multi-domain convolutional neural networks for visual tracking. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Ma C, Huang J-B, Yang X, Yang M-H (2018) Robust visual tracking via hierarchical convolutional features. IEEE Trans Pattern Anal and Mach Intell. https://doi.org/10.1109/TPAMI.2018.2865311
Candy JV (2009) Bayesian signal processing: classical modern and particle filtering methods. Wiley Interscience, New York
Mozhdehi RJ, Medeiros H (2017) Deep convolutional particle filter for visual tracking. In: 2017 IEEE International conference on image processing (ICIP)
Zitnick CL, Dollar P (2014) Edge boxes: locating object proposals from edges. In: Proceedings of European conference on computer vision
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of international conference on learning representation
Deng J, Dong W, Socher R, Li L, Li K, Li F (2009) ImageNet: a largescale hierarchical image database. In: Proceedings of IEEE conference on computer vision and pattern recognition
Vedaldi A, Lenc K (2014) MatConvNet C Convolutional Neural Networks for MATLAB. arXiv/1412.4564
Wu Y, Lim J, Yang M-H (2013) Online object tracking: a benchmark. In: IEEE conference on computer vision and pattern recognition (CVPR)
Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (No.61573095).
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Li, L., Zhang, Y., Zhou, W., Lv, S. (2020). Research on Video Target Tracking Algorithm Based on Particle Filter and CNN. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_25
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DOI: https://doi.org/10.1007/978-981-32-9682-4_25
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