Automatic detection and tracking the moving objects observed by an unmanned aerial vehicles video camera
https://doi.org/10.37661/1816-0301-2021-18-2-83-97
Abstract
An algorithm of automatic detection and tracking the moving objects for the use in equipment on board of unmanned aerial vehicles is considered. The developed algorithm is based on a tracking specially selected points for a certain period. Tracked points are selected from the areas on the current frame, where the pixel intensity differs from the intensities of the same pixels in previous frames, aligned with the current frame using projective transformation. If the displacement of the tracked points is not fixed on several adjacent frames, they are being deleted, and new points from the areas presumably belonging to moving objects in the current frame are added instead. On each frame the points similar by the location and shape of trajectories of movement are combined into groups that presumably correspond to moving objects. Objects are tracked by comparing the groups of moving points with the points of neighboring frames. Groups of moving points from neighboring frames are matched if they contain a large number of common tracked points. The algorithm allows simultaneous tracking of more than 20 objects in real time. The indication of objects as moving occurs only if during the time of its tracking it has shifted a considerable distance. The algorithm has a low percentage of false detections of moving objects, it detects well small objects and is capable reliably to accompany moving objects.
About the Author
R. S. Zhuk
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Raman S. Zhuk - Junior Researcher, the United Institute of Informatics Problems of the National Academy of Sciences of Belarus.
st. Surganova, 6, Minsk, 220012.
References
1. Chapel M.-N., Bouwmans T. Moving Objects Detection with a Moving Camera: A Comprehensive Review. Available at: https://arxiv.org/abs/2001.05238 (accessed 19.04.2021).
2. Fiaz M., Mahmood A., Jung S.-K. Tracking Noisy Targets: A Review of Recent Object Tracking Approaches. Available at: https://arxiv.org/abs/1802.03098 (accessed 19.04.2021).
3. Ye D. H., Li J., Chen Q., Wachs J., Bouman C. Deep learning for moving object detection and tracking from a single camera in unmanned aerial vehicles (UAVs). IS&T International Symposium on Electronic Imaging 2018. Burlingame, California, USA, 2018, pp. 4661-4666.
4. Lucas B., Kanade T. An iterative image registration technique with an application to stereo vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI '81). Vancouver, 1981, vol. 2, pp. 674-679.
5. Mueller M., Smith N., Ghanem B. A benchmark and simulator for uav tracking. Proceedings of the 14th European Conference on Computer Vision (ECCV '16). Amsterdam, 2016, pp. 445-461. https://doi.org/10.1007/978-3-319-46448-0_27
6. Du D., Zhu P., Wen L., Bian X., Ling H., Liu Z. VisDrone-DET2019: the vision meets drone object detection in image challenge results. IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Seoul, 2019, pp. 213-226. https://doi.org/10.1109/ICCVW.2019.00030
7. Delibasoglu I. UAV Images Dataset for Moving Object Detection from Moving Cameras. Available at: https://arxiv.org/abs/2103.11460 (accessed 19.04.2021).
8. Yi K. M., Yun K., Kim S. W., Chang H., Choi J. Detection of moving objects with non-stationary cameras in 5.8ms: bringing motion detection to your mobile device. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPR). Portland, OR, USA, 2013, pp. 27-34.
9. Yun K., Lim J., Choi J. Y. Scene conditional background update for moving object detection in a moving camera. Pattern Recognition Letters, 2017, vol. 88, pp. 57-63. https://doi.org/10.1016/j.patrec.2017.01.017
Review
For citations:
Zhuk R.S. Automatic detection and tracking the moving objects observed by an unmanned aerial vehicles video camera. Informatics. 2021;18(2):83-97. (In Russ.) https://doi.org/10.37661/1816-0301-2021-18-2-83-97
Views: 803
Email this article 