Target Tracking Algorithm based on Context-Aware Deep Feature Compression

doi:10.23940/ijpe.19.07.p6.18021812

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (7): 1802-1812.doi: 10.23940/ijpe.19.07.p6.18021812

Previous Articles Next Articles

Target Tracking Algorithm based on Context-Aware Deep Feature Compression

Ying Wang^a, Aili Wang^a,*, Ronghui Wang^b, Haiyang Liu^a, and Yuji Iwahori^c

a Higher Education Key Lab for Measuring and Control Technology and Instrumentations of Heilongjiang Harbin University of Science and Technology, Harbin, 150001, China
b Heilongjiang Province Public Security Department, Harbin, 150001, China
c Computer Science, Chubu University, Aichi, 487-8501, Japan

Submitted on ;
Contact: * E-mail address: aili925@hrbust.edu.cn
About author:Ying Wang is a master's student at Harbin University of Science and Technology. Her research interests include image superresolution and image classification.Aili Wang received her B.S. degree from the Department of Electronic and Communication Engineering and her M.S. degree and Ph.D. from the Department of Information and Communication Engineering at Harbin Institute of Technology in 2002, 2004, and 2008, respectively. She joined Harbin University of Science and Technology as an assistant in 2004 and became an associate professor in 2010. She was a visiting professor at Chubu University in 2014. Her research interests include image superresolution, image fusion, and object tracking.Ronghui Wang received his B.S. degree from the Department of Electronic and Communication Engineering at Harbin Institute of Technology in 2002.Haiyang Liu is a master's student at Harbin University of Science and Technology. Her research interests include object tracking.Yuji Iwahori received his B.S. degree from Nagoya Institute of Technology in 1983 and his M.S. degree and Ph.D. from Tokyo Institute of Technology in 1985 and 1988, respectively. He joined Nagoya Institute of Technology in 1988 and became a professor there in 2002. He joined Chubu University as a professor in 2004. He has also collaborated with UBC since 1991, IIT Guwahati since 2010, and Chulalongkorn University since 2014.

Abstract

Abstract: The main focus of target tracking is robustness and efficiency. Because of challenges such as background clutter, occlusion, and rotation, high robustness and efficiency cannot be achieved simultaneously. A tracking framework of perceptual correlation filter is improved to achieve high-speed computation between real-time trackers. The main contribution to high-speed computing speed comes from improved depth feature compression, which is realized by combining content-aware features with multiple automatic encoders. In the pre-training stage, an automatic encoder is trained for each class separately. In order to obtain the feature map suitable for target tracking, the orthogonal loss function is added to the training stage and the fine-tuning self-encoder stage. Experiments show that the improved algorithm demonstrates great improvement in accuracy and speed.

Key words: target tracking, deep learning, context-aware feature, automatic encoder

Ying Wang, Aili Wang, Ronghui Wang, Haiyang Liu, and Yuji Iwahori. Target Tracking Algorithm based on Context-Aware Deep Feature Compression [J]. Int J Performability Eng, 2019, 15(7): 1802-1812.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] D. S. Bolme, J. R. Beveridge, B. A. Draper,Y. M. Lui, “Visual Object Tracking using Adaptive Correlation Filters,” inProceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2544-2550, 2010
[2] J. Choi, H. Chang, J. Jeong, Y. Demiris,J. Y. Choi, “Visual Tracking using Attention-Modulated Disintegration and Integration,” inProceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4321-4330, 2016
[3] M. Danelljan, G. Häger, F. S. Khan,M. Felsberg, “Convolutional Features for Correlation Filter based Visual Tracking,” inProceedings of IEEE International Conference on Computer Vision Workshop, pp. 621-629, 2015
[4] M. Danelljan, G. Häger, F. S. Khan,M. Felsberg, “Learning Spatially Regularized Correlation Filters for Visual Tracking,” inProceedings of IEEE International Conference on Computer Vision, pp. 4310-4318, 2015
[5] B. Babenko, M. H. Yang,S. Belongie, “Visual Tracking with Online Multiple Instance Learning,” inProceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 983-990, 2009
[6] B. Babenko, M. H. Yang,S. Belongie, “Robust Object Tracking with Online Multiple Instance Learning,” IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 33, No. 8, pp. 1619-1632, 2011
[7] T. C. Robert, “Mean Shift Blob Tracking through Scale Space,” inProceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 234-240, 2003
[8] J. F. Henriques, R. Caseiro, P. Martins,J. Batista, “High-Speed Tracking with Kernelized Correlation Filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 3, pp. 583-596, 2015
[9] M. Danelljan, G. Hager, F. S. Khan,M. Felsberg, “Learning Spatially Regularized Correlation Filters for Visual Tracking,” inProceedings of IEEE International Conference on Computer Vision, pp. 4310-4318, 2015
[10] E. Park, H. Ju, Y. M. Jeong,S. Y. Min, “Tracking-Learning-Detection Adopted Unsupervised Learning Algorithm,” inProceedings of Seventh International Conference on Knowledge and Systems Engineering, pp. 234-237, 2015
[11] G. E. Hinton, S. Osindero,Y. W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation, Vol. 18, No. 7, pp. 1527-1554, 2006
[12] J. Zhang, S. Ma,S. S. MEEM, “Robust Tracking via Multiple Experts using Entropy Minimization,” inProceedings of European Conference on Computer Vision, pp. 188-203, 2014
[13] Y. Wu, J. Lim, and M. H. Yang, “Online Object Tracking: A Benchmark,” in Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411-2418, 2013
[14] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, et al., “Imagenet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, Vol. 115, No. 3, pp. 211-252, 2015
[15] R. Zhao, W. L. Ouyang, H. S. Li,X. G. Wang, “Saliency Detection by Multi-Context Deep Learning,” inProceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265-1274, 2015
[16] C. Ma, J. B. Huang, X. K. Yang,M. H. Yang, “Hierarchical Convolutional Features for Visual Tracking,” inProceedings of IEEE International Conference on Computer Vision, pp. 3074-3082, 2015

Target Tracking Algorithm based on Context-Aware Deep Feature Compression

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended 0

[1]	Janarthanan Sekar and Ganesh Kumar T. Hyperparameter Tuning in Deep Learning-Based Image Classification to Improve Accuracy using Adam Optimization [J]. Int J Performability Eng, 2023, 19(9): 579-586.
[2]	Sanjay Razdan, Himanshu Gupta, and Ashish Seth. D-SVM: A Deep Support Vector Machine Model with Different Kernel Types for Improved Intrusion Detection Performance [J]. Int J Performability Eng, 2023, 19(9): 598-606.
[3]	Aashita Rajput, Muskan Yadav, Sachin Yadav, Megha Chhabra, and Arun Prakash Agarwal. Patch-Based Breast Cancer Histopathological Image Classification using Deep Learning [J]. Int J Performability Eng, 2023, 19(9): 607-623.
[4]	Kavita Pandey, and Dhiraj Pandey. Real-Time Crop Disease Detection and Remedial Suggestion through Deep Learning-based Smartphone Application [J]. Int J Performability Eng, 2023, 19(8): 491-498.
[5]	Savita Khurana, Gaurav Sharma, and Bhawna Sharma. Hybrid Machine Learning Model for Load Prediction in Cloud Environment [J]. Int J Performability Eng, 2023, 19(8): 507-515.
[6]	Manvi Khatri and Ajay Sharma. Deep Learning Approach based on Iris, Face, and Palmprint Fusion for Multimodal Biometric Recognition System [J]. Int J Performability Eng, 2023, 19(6): 407-416.
[7]	Shreshtha Singh and Arun Sharma. State of the Art Convolutional Neural Networks [J]. Int J Performability Eng, 2023, 19(5): 342-349.
[8]	Vaishali Arya and Tapas Kumar. Boosting X-Ray Scans Feature for Enriched Diagnosis of Pediatric Pneumonia using Deep Learning Models [J]. Int J Performability Eng, 2023, 19(3): 175-183.
[9]	Shikha Choudhary and Bhawna Saxena. Image-Based Crop Disease Detection using Machine Learning Approaches: A Survey [J]. Int J Performability Eng, 2023, 19(2): 122-132.
[10]	Pavneet Singh, Jigyasa Chopra, Amandeep Singh, Nikita Nijhawan, and Kritika. Deep Learning Innovations for Enhanced Drusen Detection in Retinal Images [J]. Int J Performability Eng, 2023, 19(12): 779-787.
[11]	Mansi Mahendru and Sanjay Kumar Dubey. Portable Learning Approach towards Capturing Social Intimidating Activities using Big Data and Deep Learning Technologies [J]. Int J Performability Eng, 2022, 18(9): 668-678.
[12]	Sandhya Alagarsamy and Visumathi James. RNN LSTM-based Deep Hybrid Learning Model for Text Classification using Machine Learning Variant XGBoost [J]. Int J Performability Eng, 2022, 18(8): 545-551.
[13]	K. Lavanya, Smrithi Prakash, Yash Gedam, Altamash Aijaz, and L. Ramanathan. Real Time Digital Face Mask Detection using MobileNet-V2 and SSD with Apache Spark [J]. Int J Performability Eng, 2022, 18(8): 598-604.
[14]	Rajan Prasad Tripathi, Sunil Kumar Khatri, and Darelle Van Greunen. Relative Examination of Breast Malignant Growth Analysis Utilizing Different Machine Learning Algorithms [J]. Int J Performability Eng, 2022, 18(6): 417-425.
[15]	Poonam Narang, Ajay Vikram Singh, and Himanshu Monga. Hybrid Metaheuristic Approach for Detection of Fake News on Social Media [J]. Int J Performability Eng, 2022, 18(6): 434-443.