FCN and Siamese Network for Small Target Tracking in Forward-looking Sonar Images

In underwater forward-looking sonar images, a small moving target is susceptible to noise pollution, and the performance of object tracking is greatly affected by background disturbances, illumination changes and occlusion. Hence, we propose to combine FCN and Siamese network for small moving target tracking. In order to solve the problem of too few data sets, we use geometric transformation methods to extend the data sets. In the other side, we adopt the FCN network structure, it can accept any size of input forward-looking sonar images and make tracking more efficient. Moreover, by using the Siamese network structure and removing the last full connected layer, it enables tracking more accurately. The reduction in the number of network layers also greatly improves real-time performance. The experimental results show that our method is very suitable for small moving target tracking in forward-looking sonar images and there is no target tracking loss occurred. It overcomes the noise interference in forward-looking sonar images, and significantly improves the accuracy and real-time performance.

[1]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[2]  Michael Felsberg,et al.  The Visual Object Tracking VOT2015 Challenge Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[3]  Zhe Chen,et al.  MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Yann LeCun,et al.  Computing the stereo matching cost with a convolutional neural network , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Gregory R. Koch,et al.  Siamese Neural Networks for One-Shot Image Recognition , 2015 .

[6]  Iasonas Kokkinos,et al.  Discriminative Learning of Deep Convolutional Feature Point Descriptors , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Lei Zhang,et al.  Object Tracking via Dual Linear Structured SVM and Explicit Feature Map , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Michael Felsberg,et al.  Learning Spatially Regularized Correlation Filters for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Thomas Brox,et al.  FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[11]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[12]  Lei Luo,et al.  Enable Scale and Aspect Ratio Adaptability in Visual Tracking with Detection Proposals , 2015, BMVC.

[13]  Dit-Yan Yeung,et al.  Understanding and Diagnosing Visual Tracking Systems , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Luca Bertinetto,et al.  Staple: Complementary Learners for Real-Time Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).