Tiny moving vehicle detection in satellite video with constraints of multiple prior information

References

• Ahmadi, S. A., A. Ghorbanian, and A. Mohammadzadeh. 2019. “Moving Vehicle Detection, Tracking and Traffic Parameter Estimation from a Satellite Video: A Perspective on a Smarter City.” International Journal of Remote Sensing 40 (22): 8379–8394. doi:10.1080/01431161.2019.1610983.
   Web of Science ®Google Scholar
• Ahmadi, S. A., and A. Mohammadzadeh. 2017. “A Simple Method for Detecting and Tracking Vehicles and Vessels from High Resolution Spaceborne Videos.” In 2017 Joint Urban Remote Sensing Event (Jurse), Dubai, UAE, 1–4.
   Google Scholar
• Ammar, A., A. Koubaa, M. Ahmed, and A. Saad. 2019. “Aerial Images Processing for Car Detection Using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3.” arXiv: Computer Vision and Pattern Reeognition.
   Google Scholar
• Deng, W., and W. Yin. 2016. “0n the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers.” Journal of Scientific Computing 66 (3): 889–916. doi:10.1007/s10915-015-0048-x.
   Web of Science ®Google Scholar
• Du, B., Y. Sun, S. Cai, C. Wu, and Q. Du. 2018. “object Tracking in Satellite Videos by Fusing the Kernel Correlation Filter and the Three-Frame-Difference Algorithm.” IEEE Geoscience and Remote Sensing Letters 15 (2): 168–172.
   Web of Science ®Google Scholar
• He, R., B. Hu, W. Zheng, and X. Kong. 2011. “Robust Principal Component Analysis Based on Maximum Correntropy Criterion.” IEEE Transactions on Image Proceessing 20 (6): 1485–1494. doi:10.1109/TIP.2010.2103949.
   PubMed Web of Science ®Google Scholar
• Ju, J., and J. xing. 2019. “Moving Object Detection Based on Smoothing Three Frame Difference Method Fused with RPCA.” Multimedia Tools and Applieations 78 (21): 29937–29951. doi:10.1007/s11042-018-6710-1.
   Web of Science ®Google Scholar
• Mou, L., X. Zhu, M. Vakalopoulou, K. Karantzalos, N. Paragios, B. Le Saux, G. Moser, and D. Tuia. 2017. “Multitemporal Very High Resolution from Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (8): 3435–3447. doi:10.1109/JSTARS.2017.2696823.
   Web of Science ®Google Scholar
• Narayanamurthy, P., and N. Vaswani. 2019. “Provable Dynamic Robust PCA or Robust Subspace Tracking.” IEEE Transactions on Information Theory 65 (3): 1547–1577. doi:10.1109/TIT.2018.2872023.
   Web of Science ®Google Scholar
• Pollard, T., and M. Antone. 2012. “Detecting and Tracking All Moving Objects in Wide-area Aerial Video.” In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA, 15–22.
   Google Scholar
• Qin, X., Z. Zhang, C. Huang, C. Gao, M. Dehghan, and M. Jagersand. 2019. “Basnet: Boundary-aware Salient Object Detection.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Reeognition, Long Beach, CA, USA, 7479–7489.
   Google Scholar
• Shi, F., F. Qiu, X. Li, Y. Tang, R. Zhong, and C. Yang. 2020. “A Method to Detect and Track Moving Airplanes from A Satellite Video.” Remote Sensing 12 (15): 2390. doi:10.3390/rs12152390.
   Web of Science ®Google Scholar
• Sommer, L. W., M. Teutsch, T. Schuchert, and Beyerer. 2016. “A Survey on Moving Object Detection for Wide Area Motion Imagery.” In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 1–9.
   Google Scholar
• Wang, Y., J. Lee, and B. Li. 2015. “A Hybrid Illumination Refinement Technique Based on Double Niblack Thresholds.” International Journal of Future Computer and Communieation 4 (6): 426–430. doi:10.18178/ijfcc.2015.4.6.430.
   Google Scholar
• Wang, X., A. Shrivastava, and A. Gupta. 2017. “A-fast-rcnn: Hard Positive Generation via Adversary for Object Detection.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Reeognition, Honolulu, HI, 2606–2615.
   Google Scholar
• Xiang, X., M. Zhai, N. Lv, and A. E. Saddik. 2018. “Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos.” sensors 18 (8): 2560. doi:10.3390/s18082560.
   Web of Science ®Google Scholar
• Xiao, J., R. Zhu, R. Hu, M. Wang, Y. Zhu, D. Chen, and D. Li. 2018. “Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing.” Remote Sensing 10 (6): 876. doi:10.3390/rs10060876.
   Web of Science ®Google Scholar
• Yang, T., X. Wang, B. Yao, J. Li, Y. Zhang, Z. He, and W. Duan. 2016. “Small Moving Vehicle Detection in a Satellite Video of an Urban Area.” sensors 16 (9): 1528. doi:10.3390/s16091528.
   PubMed Web of Science ®Google Scholar
• Yuanbin, W., and R. Jieying. 2019. “An Improved Vibe Based on Gaussian Pyramid.” In 2019 4th International Conference on Control and Robotics Engineering (ICCRE), Nanjing, China, 105–109.
   Google Scholar
• Zhang, J., S. Sclarof, Z. Lin, X. Shen, B. Price, and R. Mech. 2015. “Minimum Barrier Salient Object Detection at 80 Fps.” In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 1404–1412.
   Google Scholar
• Zhang, J., X. Jia, and J. Hu. 2019. “Local Region Proposing for Frame-Based Vehicle Detection in Satellite videos ”.” Remote Sensing 11 (20): 2372. doi:10.3390/rs11202372.
   Web of Science ®Google Scholar
• Zhao, J., J. Liu, D. Fan, Y. Cao, J. Yang, and M. Cheng. 2019. “EGNet: Edge Guidance Network for Salient 0bject Detection.” In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 8778–8787.
   Google Scholar
• Zhu, Ｗ., S. Liang, Y. Ｗei, and J. Sun. 2014. “Saliency Optimiza-tion from Robust Background Detection.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Reeognition, Columbus, OH, USA, 2814–2821.
   Google Scholar
• Zhu, Q., L. Jing, and R. Bi. 2010. “Exploration and Improvement of 0stu Threshold Segmentation Algorithm.” In 2010 8th World Congress on Intelligent Control and Automation, Jinan, China, 6183–6188.
   Google Scholar