MODEL AND TRAINING METHOD OF MOVING OBJECT CLASSIFICATION SYSTEM FOR A COMPACT DRONE
Abstract
Keywords
Full Text:
PDF (Українська)References
Luo, C., Nightingale, J., Asemota, E., Grecos, C. A UAV-Cloud System for Disaster Sensing Applications, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, 2015, pp. 1–5. DOI: 10.1109/VTCSpring.2015.7145656.
Wang, J., Feng, Z., Chen, Z., George, S., Bala, M., Pillai, P., Yang, S., Satyanarayanan, M. Bandwidth-Efficient Live Video Analytics for Drones Via Edge Computing, 2018 IEEE/ACM Symposium on Edge Computing (SEC), Bellevue, WA, 2018, pp. 159–173. DOI:10.1109/SEC.2018.00019.
Savitha, C., Ramesh, D. Motion detection in video surviellance: A systematic survey, 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2018, pp. 51–54. DOI: 10.1109/ICISC.2018.8398880.
Wu-ChihHu, Chao-HoChen, Tsong-YiChen, Deng-YuanHuang, Zong-CheWu, Moving object detection and tracking from video captured by moving camera Journal of Visual Communication and Image Representation, Elsevier, vol. 30, pp. 164–180. DOI:10.1016/j.jvcir.2015.03.003.
Kim, S. W., Yun, K., Yi, K. M., Kim, S. J., Choi, J. Y. Detection of moving objects with a moving camera using non-panoramic background model, Machine Vision and Applications, 2013, vol. 24, iss. 5, pp 1015–1028. DOI:10.1007/s00138-012-0448-y.
Logoglu, K., Lezki, H., Yucel, M. Feature-based efficient moving object detection for low-altitude aerial platforms, 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), Venice, Italy, 2017, pp. 2119–2128. DOI: 10.1109/ICCVW.2017.248.
Okafor, E., Pawara, P. Comparative study between deep learning and bag of visual words for wild-animal recognition, 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, 2016, pp. 1–8. DOI: 10.1109/SSCI.2016.7850111.
Lemley, J., Bazrafkan, S., Corcoran, P. Smart augmentation learning an optimal data augmentation strategy, IEEE Access, 2017, vol. 5, pp. 5858–5869. DOI: 10.1109/ACCESS.2017.2696121.
Labusch, K., Barth, E., Martinetz, T. Sparse coding neural gas: learning of overcomplete data representations, Neurocomputing, 2009, vol. 72, iss. 7–9, pp. 1547–1555. DOI:10.1016/j.neucom.2008.11.027.
Ayumi, V., Rere, L. M. R., Fanany, M. I., Arymurthy, A. M. Optimization of convolutional neural network using microcanonical annealing algorithm, 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Malang, 2016, pp. 506 – 511. DOI: 10.1109/ICACSIS.2016.7872787.
Moskalenko, V., Moskalenko, A., Korobov, A., Semashko, V. The model and training algorithm of compact drone autonomous visual navigation system, Data, 2019, vol. 4, iss. 1, DOI: 10.3390/data4010004.
Moskalenko, V., Dovbysh, S., Naumenko, I., Moskalenko, A., Korobov, A. Improving the effectiveness of training the on-board object detection system for a compact unmanned aerial vehicle, Eastern-European Journal of Enterprise Technologies, 2018, vol. 4, no.9 (94), pp. 19–26. DOI: 10.15587/1729-4061.2018.139923.
Montoya-Catalá, M., Alvear-Sandoval, R. F., Figueiras-Vidal, A. R. Experiments in combining boosting and deep stacked networks, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietri sul Mare, 2016, pp. 1–6. DOI: 10.1109/MLSP.2016.7738874.
Tareen, S. A. K., Saleem, Z. A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK, 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, 2018, pp. 1–10. DOI: 10.1109/ICOMET.2018.8346440.
Raid, A. M., Khedr, W., El-dosuky, M., Aoud, M. Image restoration based on morphological operations. International Journal of Computer Science, Engineering and Information Technology, vol. 4, pp. 9–21, 2014. DOI: 10.5121/ijcseit.2014.4302.
Xu, T., Huang, C., He, Q., Guan, G., Zhang, Y. An improved TLD target tracking algorithm, 2016 IEEE International Conference on Information and Automation (ICIA), Ningbo, 2016, pp. 2051–2055. DOI: 10.1109/ICInfA.2016.7832157.
Wang, D.-w., Ma, X., Su, Y. Undercomplete dictionary-based feature extraction for radar target identification, Progress in Electromagnetics Research M, vol. 1, 2008, pp. 1–19. DOI: 10.2528/PIERM08012805.
Luo Z. MIO-TCD: A new benchmark dataset for vehicle classification and localization, in IEEE Transactions on Image Processing, vol. 27, no. 10, pp. 5129–5141, Oct. 2018. DOI: 10.1109/TIP.2018.2848705.
Oh, S., Hoogs, A., Amitha Perera, A. G., Cuntoor, N. A large-scale benchmark dataset for event recognition in surveillance video, 2011 Computer Vision and Pattern Recognition (CVPR), IEEE, 2011, pp. 3153–3160. DOI:10.1109/CVPR.2011.5995586.
MIO-TCD dataset. Available at: http://podoce.dinf.usherbrooke.ca/challenge/dataset/ (accessed 24.05.2019).
VIRAT Video Dataset. Available at: http://www.viratdata.org/ (accessed 24.05.2019).
DOI: https://doi.org/10.32620/reks.2019.2.10
Refbacks
- There are currently no refbacks.