IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지
DOI QR코드

DOI QR Code

Reinforced Feature of Dynamic Search Area for the Discriminative Model Prediction Tracker based on Multi-domain Dataset

다중 도메인 데이터 기반 구별적 모델 예측 트레커를 위한 동적 탐색 영역 특징 강화 기법

  • Lee, Jun Ha (Korea Institute of Industrial Technology, Kyungpook National University) ;
  • Won, Hong-In (Korea Institute of Industrial Technology) ;
  • Kim, Byeong Hak (Korea Institute of Industrial Technology)
  • Received : 2021.07.22
  • Accepted : 2021.08.14
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.

Keywords

Acknowledgement

본 논문은 한국생산기술연구원 기관주요사업 "멀티 도메인 비전 시스템 AI 인식 기술 연구 (kitech EI-21-0019)"의 지원으로 수행한 연구입니다.

References

  1. L. Hou, W. Wan, K. H. Lee, J. N. Hwang, G. Okopal, J, Pitton, "Robust Human Tracking Based on DPM Constrained Multiple-kernel from a Moving Camera," Journal of Signal Processing Systems, Vol. 86, No. 1, pp. 27-39, 2017. https://doi.org/10.1007/s11265-015-1097-y
  2. S. J. Lee, B. H. Kim, M. Y. Kim, "Multi-Saliency Map and Machine Learning Based Human Detection for the Embedded Top-View Imaging System," IEEE Access, Vol. 9, pp. 70671-70682, 2021. https://doi.org/10.1109/ACCESS.2021.3078623
  3. L. Bertinetto, J. Valmadre. J. F. Henriques. A. Vedaldi, P. H. S. Torr, "Fully-convolutional Siamese Networks for Object Tracking," European Conference on Computer Vision. Springer, Cham, pp. 850-865, 2016.
  4. G. Bhat, M. Danelljan, L. V. Gool, R. Timofte, "Learning Discriminative Model Prediction for Tracking," Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6182-6191, 2019.
  5. M. Danelljan, G. Bhat, F. S. Khan, M. Felsberg, "Atom: Accurate Tracking by Overlap Maximization," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4660-4669, 2019.
  6. B. Li, J. Yan, W. Wu, Z. Zhu, X. Hu., "High Performance Visual Tracking with Siamese Region Proposal Network," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8971-8980, 2018.
  7. M. Danelljan, L. V. Gool, R. Timofte, "Probabilistic Regression for Visual Tracking," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7183-7192, 2020.
  8. B. H. Kim, A. Lukezic, J. H. Lee, H. M. Jung, M. Y. Kim, "Global Motion-aware Robust Vobject Tracking for Electro Optical Targeting Systems," Sensors, Vol. 20, No. 2, pp. 566, 2020. https://doi.org/10.3390/s20020566
  9. B. H. Kim, D. Khan, C. Bohak, W. Choi, H. J. Lee, M. Y. Kim, "V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System," Sensors, Vol. 18, No. 11, pp. 3825, 2018. https://doi.org/10.3390/s18113825
  10. B. H. Kim, D. Khan, W. Choi, M. Y. Kim, "Real-time Counter-UAV System for Long Distance Small Drones Using Double Pan-tilt Scan Laser Radar," Laser Radar Technology and Applications XXIV. International Society for Optics and Photonics, pp. 11005, 2019.
  11. K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang, C. C. Loy, D. Lin, "Hybrid Task Cascade for Instance Segmentation," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4974-4983, 2019.
  12. M. Kristan, A. Leonardis, J. Matas, M. Felsberg, "The Eighth Visual Object Tracking VOT2020 Challenge Results," European Conference on Computer Vision. Springer, Cham, pp. 547-601, 2020.
  13. L. Cehovin, "TraX: The Visual Tracking Exchange Protocol and Library," Neurocomputing, Vol. 260, pp. 5-8, 2017. https://doi.org/10.1016/j.neucom.2017.02.036
  14. B. H. Kim, M. Y. Kim, Y. S. Chae, "Background Registration-based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications," Sensors, Vol. 18, No. 1, pp. 60, 2018. https://doi.org/10.3390/s18010060
  15. M. Z. Mekuc, C. Bohak, S. Hudoklin, B. H. Kim , R. Romih, M. Y. Kim, M. Marolt, "Automatic Segmentation of Mitochondria and Endolysosomes in Volumetric Electron Microscopy Data," Computers in Biology and Medicine, Vol. 119, pp. 103693, 2020. https://doi.org/10.1016/j.compbiomed.2020.103693
  16. B. H. Kim, C. Bohak, K. H. Kwon, M. Y. Kim, "Cross Fusion-based low Dynamic and Simage Enhancement for Infrared Search and Tracking Systems," IEEE Access, Vol. 8, pp. 15347-15359, 2020. https://doi.org/10.1109/access.2020.2966794
  17. H. M. Jung, B. H. Kim, M. Y. Kim, "Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration," IEEE Access, Vol. 8, pp. 145401-145412, 2020. https://doi.org/10.1109/access.2020.3011580
  18. N. Dalal, B. Triggs, "Histograms of Oriented Gradients for Human Detection," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Ieee, Vol. 1, pp. 886-893, 2005.
  19. T. Lindeberg , "Scale Invariant Feature Transform," pp. 10491, 2012.
  20. H. Bay, A. Ess, T. Tuytelaars, L. V. Gool, "Surf: Speeded up Robust Features." European conference on computer vision, Springer, Berlin, Heidelberg, Vol. 110, No. 3, pp. 346-359, 2006.
  21. E. Rosten, T. Drummond, "Machine Learning for High-speed Corner Detection," European conference on computer vision. Springer, Berlin, Heidelberg, pp. 430-443, 2006.
  22. S. Kamthania, "A Novel Deep Learning rbm Based Algorithm for Securing Containers," in 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 1-7, 2019.
  23. W. Li, Z. Dou, L. Qi, "Communication Protocol Classification Based on lstm and dbn," IEEE Access, Vol. 8, pp. 91818-91828, 2020. https://doi.org/10.1109/access.2020.2979768
  24. I. El-Feghi, A. Tahar, M. Ahmadi, "Efficient Features Extraction for Fingerprint Classification with Multi Layer Perceptron Neural Network," in Eighth International Multi-Conference on Systems, Signals Devices, pp. 1-4, 2011.
  25. H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, R. M. Summers, "Deep Convolutional Neural Networks for Computer-aided Detection: Cnn Architectures, Dataset Characteristics and Transfer Learning," IEEE, Vol. 35. No. 5, pp. 1285-1298, 2016.
  26. P. Liu, H. Zhang, K. Zhang, L. Lin, W. Zuo, "Multi-level Wavelet-CNN for Image Restoration", Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), pp. 773-782, 2018.
  27. H. M. Jung, B. H. Kim, M. Y. Kim, "Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration," IEEE Access, Vol. 8, pp. 145401-145412, 2020. https://doi.org/10.1109/access.2020.3011580
  28. S. C. Huang, C. Fan-Chieh, C. Yi-Sheng, "Efficient Contrast Enhancement Using Adaptive Gamma Correction with Weighting Distribution," IEEE Transactions on Image Processing, Vol. 22, No. 3, pp. 1032-1041, 2012. https://doi.org/10.1109/TIP.2012.2226047
  29. Y. Wang, Q. Chen, B. Zhang, "Image Enhancement Based on Equal Area Dualistic Sub-image Histogram Equalization Method," IEEE transactions on Consumer Electronics, Vol. 45, No. 1, pp. 68-75, 1999. https://doi.org/10.1109/30.754419
  30. H. Fan, L. Lin, F. Yang, P. Chu, G. Deng, "Lasot: A high-quality benchmark for large-scale single object tracking," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5374-5383, 2019.
  31. L. Huang, X. Zhao, K. Huang, "Got-10k: A Large High-diversity Benchmark for Generic Object Tracking in the Wild," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
  32. M. Muller, A. Bibi, S. Giancola, S. Alsubaihi, B. Ghanem, "Trackingnet: A Large-scale Dataset and Benchmark for Object Tracking in the Wild," Proceedings of the European Conference on Computer Vision (ECCV), pp. 300-317, 2018.
  33. T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, C. L. Zitnick, "Microsoft coco: Common Objects in Context," European Conference on Computer Vision. Springer, Cham, pp. 740-755, 2014.