Abstract
In this article, parallel implementation of a real-time intelligent video surveillance system on Graphics Processing Unit (GPU) is described. The system is based on background subtraction and composed of motion detection, camera sabotage detection (moved camera, out-of-focus camera and covered camera detection), abandoned object detection, and object-tracking algorithms. As the algorithms have different characteristics, their GPU implementations have different speed-up rates. Test results show that when all the algorithms run concurrently, parallelization in GPU makes the system up to 21.88 times faster than the central processing unit counterpart, enabling real-time analysis of higher number of cameras.
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Abbreviations
- CPU:
-
Central processing unit
- GPU:
-
Graphics processing unit
- CCD:
-
Covered camera detection
- MCD:
-
Moved camera detection
- OOFCD:
-
Out-of-focus camera detection
- VMD:
-
Video motion detection
- GMM:
-
Gaussian mixture model
- IAGMM:
-
Improved adaptive Gaussian mixture model
- VSAM:
-
Video surveillance and monitoring
- AOD:
-
Abandoned object detection
- OT:
-
Object tracking
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Acknowledgments
This research was funded by Ministry of Science, Industry and Technology SAN-TEZ program grant number 00542.STZ.2010-1. We also would like to thank NVIDIA for their donation of Tesla C2075 GPU boards.
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Guler, P., Emeksiz, D., Temizel, A. et al. Real-time multi-camera video analytics system on GPU. J Real-Time Image Proc 11, 457–472 (2016). https://doi.org/10.1007/s11554-013-0337-2
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DOI: https://doi.org/10.1007/s11554-013-0337-2