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Algorithm for detection of moving objects observed by a video camera

https://doi.org/10.29235/1561-8323-2023-67-1-20-26

Abstract

An algorithm to detect moving objects captured by a moving video camera is presented. The algorithm is based on detection of motion on video frames taken by a moving video camera, as well as on finding and analyzing the trajectories of moving objects. A feature of the algorithm is detection on frames of connected areas (clusters) of possible object motion. Then moving points on the detected clusters are found, and those points trajectories are built with help of the optical flow. The trajectories are used as features of moving objects. Only smooth trajectories are exploited for detection of moving objects, and the remaining ones are removed from consideration. An object is considered as moving on the current frame if it contains ends of a sufficient number of trajectories of moving points found on previous frames. The presented algorithm has a low computational complexity, which allows it to be used in real or near real time on small computers that have only a few processors of the ARM architecture without powerful parallel computing tools such as GPUs or neural network processors NPU.

About the Author

B. A. Zalesky
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus

Zalesky Boris A. – D. Sc. (Physics and Mathematics), Head of the Laboratory

6, Surganov Str., 220012, Minsk



References

1. Chapel M.-N., Bouwmans T. Moving Objects Detection with a Moving Camera: A Comprehensive Review. Computer Science Review, 2020, vol. 38, art. 100310. https://doi.org/10.1016/j.cosrev.2020.100310

2. Motion Detection. Available at: https://paperswithcode.com/task/motion-detection (accessed 25 May 2022).

3. Zhuk R. S. Аutomatic detection and tracking the moving objects observed by an unmanned aerial vehicles video camera. Informatics, 2021, vol. 18, no. 2. pp. 83–97 (in Russian). https://doi.org/10.37661/1816-0301-2021-18-2-83-97 26

4. Gonzales R. C., Woods R. E. Digital Image Processing. Forth Edition. Pearson/Prentice-Hall. 2018. 1192 p.

5. Wojke N., Bewley A., Paulus D. Simple online and realtime tracking with a deep association metric. ICIP’17: Proceedings of 2017 IEEE International Conference on Image Processing. 2017, pp. 3645–3650. https://doi.org/10.1109/ icip.2017.8296962


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ISSN 1561-8323 (Print)
ISSN 2524-2431 (Online)