@Article{10798587.2016.1267251,
AUTHOR = {Zhiguo Yan, Zheng Xu, Jie Dai},
TITLE = {The Big Data Analysis on the Camera-based Face Image in Surveillance Cameras*},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {1},
PAGES = {123--132},
URL = {http://www.techscience.com/iasc/v24n1/39733},
ISSN = {2326-005X},
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.},
DOI = {10.1080/10798587.2016.1267251}
}