Real-time multi-camera video analytics system on GPU

Date

2016-03-01

Author

Güler, Puren
Emeksiz, Deniz
Temizel, Alptekin
Teke, Mustafa
Temizel, Tugba Taskaya

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Video Surveillance, Video Analytics, Real-Time, CUDA, GPU

URI

https://hdl.handle.net/11511/31463

Journal

JOURNAL OF REAL-TIME IMAGE PROCESSING

DOI

https://doi.org/10.1007/s11554-013-0337-2

Collections

Graduate School of Informatics, Article

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Citation Formats

P. Güler, D. Emeksiz, A. Temizel, M. Teke, and T. T. Temizel, “Real-time multi-camera video analytics system on GPU,” JOURNAL OF REAL-TIME IMAGE PROCESSING, pp. 457–472, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31463.