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The Big Data Analysis on the Camera-based Face Image in Surveillance Cameras*

Zhiguo Yan, Zheng Xu, Jie Dai

Department of Internet of Things, The Third Research Institute of the Ministry of Public Security, Shanghai, China

* Corresponding Author: Zheng Xu, email

Intelligent Automation & Soft Computing 2018, 24(1), 123-132. https://doi.org/10.1080/10798587.2016.1267251

Abstract

In the Big-Data era, currently how to automatically realize acquisition, refining and fast retrieval of the target information in a surveillance video has become an urgent demand in the public security video surveillance field. This paper proposes a new gun-dome camera cooperative system, which solves the above problem partly. The system adopts a master-slave static panorama-variable view dualcamera cooperative video-monitoring system. In this dual-camera system the gun camera static camera) with a wide viewing -angle lenses is in charge of the pedestrian detection and the dome camera can maneuver its focus and cradle orientation to get the clear and enlarged face images. In the proposed architecture, Deformable Part Model (DPM) method realizes real-time detection of pedestrians. The look-up table method is proved feasible in a dual-camera cooperative calibration procedure, while the depth information of the moving target changes slightly. As respect to the face detection, the deep learning architecture is exploited and proves its effectiveness. Moreover, we utilize the Haar-Like feature and LQV classifier to execute the frontal face image capture. The experimental results show the effectiveness and efficiency of the dual-camera system in close-up face image acquisition.

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Cite This Article

Z. Yan, Z. Xu and J. Dai, "The big data analysis on the camera-based face image in surveillance cameras*," Intelligent Automation & Soft Computing, vol. 24, no.1, pp. 123–132, 2018.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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