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A fast moving object recognition method for video surveillance system
Author(s): Bo Lei; Qiang Fan; Hai Tan
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Paper Abstract

Prevailing object detection algorithms such as RCNN, YOLO and SSD usually are not suitable for high definition surveillance systems because of the fixed size of network input and masses of object candidate regions in the select search process. This paper proposes a fast moving object detection and recognition method for video surveillance system, which applies background extraction and frame difference to fulfill select search process, followed by a pretrained CNN model inference to complete object recognition. Proposed method was proved to be fast and effective in our experimental results which is more suitable for moving object detection for video surveillance system, compared to current other object detection algorithms.

Paper Details

Date Published: 20 November 2019
PDF: 7 pages
Proc. SPIE 11192, Real-time Photonic Measurements, Data Management, and Processing IV, 1119211 (20 November 2019); doi: 10.1117/12.2537663
Show Author Affiliations
Bo Lei, Wuhan National Research Ctr. for Optoelectronics (China)
Qiang Fan, Wuhan National Research Ctr. for Optoelectronics (China)
Hai Tan, Wuhan National Research Ctr. for Optoelectronics (China)


Published in SPIE Proceedings Vol. 11192:
Real-time Photonic Measurements, Data Management, and Processing IV
Ming Li; Bahram Jalali; Mohammad Hossein Asghari, Editor(s)

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