Fuzzy-aided solution for out-of-view challenge in visual tracking under IoT-assisted complex environment

With the rapid development in computer vision domain, research on object tracking has directed more attention by scholars. Out of view (OV) is an important challenge often encountered in the tracking process of objects, especially in Internet of Things surveillance. Therefore, this paper proposes a fuzzy-aided solution for OV challenge. This solution uses a fuzzy-aided system to detect whether the target is poorly tracked by using the response matrix of samples. When poor tracking occurs, the target is relocated according to the stored template. The proposed solution is tested on OTB100 dataset, where the experimental results show that the auxiliary solution is effective for the OV challenge. The proposed solution also ensures the tracking speed and overall success rate of visual tracking as well as improves the robustness to a certain extent for IoT-assisted complex environment.

[1]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Michael Felsberg,et al.  Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking , 2016, ECCV.

[3]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[5]  Sherali Zeadally,et al.  GSTR: Secure Multi-hop Message Dissemination in Connected Vehicles using Social Trust Model , 2019, Internet Things.

[6]  Dan Schonfeld,et al.  Fast object tracking using adaptive block matching , 2005, IEEE Transactions on Multimedia.

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

[8]  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).

[9]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Wenyao Xu,et al.  QoE-Driven Content-Centric Caching With Deep Reinforcement Learning in Edge-Enabled IoT , 2019, IEEE Computational Intelligence Magazine.

[11]  Jianke Zhu,et al.  A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration , 2014, ECCV Workshops.

[12]  Mohammed Atiquzzaman,et al.  VANETomo: A congestion identification and control scheme in connected vehicles using network tomography , 2020, Comput. Commun..

[13]  Muhammad Sajjad,et al.  Human Behavior Understanding in Big Multimedia Data Using CNN based Facial Expression Recognition , 2019, Mob. Networks Appl..

[14]  Shuai Liu,et al.  A review of visual moving target tracking , 2017, Multimedia Tools and Applications.

[15]  Michael Felsberg,et al.  Adaptive Color Attributes for Real-Time Visual Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Vijayakumar Singanamalla,et al.  Reliable and energy‐efficient emergency transmission in wireless sensor networks , 2019, Internet Technol. Lett..

[17]  A.M. Tekalp,et al.  Multi-view spatial integration and tracking with Bayesian networks , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[18]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[19]  Wei Wu,et al.  Distractor-aware Siamese Networks for Visual Object Tracking , 2018, ECCV.

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

[21]  Gang Wang,et al.  Real-time part-based visual tracking via adaptive correlation filters , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Jiri Matas,et al.  Online learning of robust object detectors during unstable tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[23]  Ming-Hsuan Yang,et al.  Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Khan Muhammad,et al.  DeepReS: A Deep Learning-Based Video Summarization Strategy for Resource-Constrained Industrial Surveillance Scenarios , 2020, IEEE Transactions on Industrial Informatics.

[25]  A. Murat Tekalp,et al.  Tracking visible boundary of objects using occlusion adaptive motion snake , 2000, IEEE Trans. Image Process..

[26]  Horst Bischof,et al.  On-line boosting-based car detection from aerial images , 2008 .

[27]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[28]  Vibhav Vineet,et al.  Struck: Structured Output Tracking with Kernels , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Song Guo,et al.  Green Resource Allocation Based on Deep Reinforcement Learning in Content-Centric IoT , 2018, IEEE Transactions on Emerging Topics in Computing.

[30]  Bruce A. Draper,et al.  Visual object tracking using adaptive correlation filters , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Shai Avidan,et al.  Locally Orderless Tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Zhenzhong Wei,et al.  Discriminative descriptors for object tracking , 2016, J. Vis. Commun. Image Represent..