Research on target tracking based on convolutional networks

In this paper, aiming at the application of target tracking, an improved convolutional network Siamese-MF (multi-feature Siamese networks) based on Siamese-FC (fully-convolutional Siamese networks) is proposed to further improve the tracking speed and accuracy to meet the requirements of target tracking in engineering applications. For tracking networks, considering the trade-off between speed and accuracy, reducing computational complexity and increasing the receptive field of convolution feature are the directions to improve the speed and accuracy of tracking networks. There are two main points to improve the structure of convolution network: 1) introducing feature fusion to enrich features; 2) introducing dilated convolution to reduce the amount of computation and enhance the field of perception. Siamese-MF algorithm achieves real-time and accurate tracking of targets in complex scenes. The average speed of testing on OTB of public data sets reaches 76 f/s, the average value of overlap reaches 0.44, and the average value of accuracy reaches 0.61. The real-time, accuracy and stability are improved to meet the requirement in real-time target tracking application.

[1]  Zhang Jianlin,et al.  Dim small target tracking based on improved particle filter , 2018 .

[2]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  K. Misawa,et al.  20-fps motion capture of phase-controlled wave-packets for adaptive quantum control , 2006 .

[4]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[6]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[7]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[9]  Luca Bertinetto,et al.  Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.

[10]  Zhang Jianlin,et al.  Application of aircraft target tracking based on deep learning , 2019 .

[11]  Li Yan,et al.  An optoelectronic system for fast search of low slow small target in the air , 2018 .

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Silvio Savarese,et al.  Learning to Track at 100 FPS with Deep Regression Networks , 2016, ECCV.

[14]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[16]  Jean Ponce,et al.  Unsupervised Object Discovery and Tracking in Video Collections , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[17]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[18]  Bohyung Han,et al.  Modeling and Propagating CNNs in a Tree Structure for Visual Tracking , 2016, ArXiv.

[19]  Ming-Hsuan Yang,et al.  Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[20]  Xiaogang Wang,et al.  Visual Tracking with Fully Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Dit-Yan Yeung,et al.  Learning a Deep Compact Image Representation for Visual Tracking , 2013, NIPS.