Human Detection in Intelligent Video Surveillance: A Review
Li Hou*,**, Qi Liu*,**, Zhenhai Chen*, and Jun Xu***
*School of Information Engineering, Huangshan University
39 Xihai Road, Tunxi District, Huangshan 245041, China
**School of Communication and Information Engineering, Shanghai University
99 Shangda Road, Baoshan District, Shanghai 200444, China
***Department of Engineering, Daqing Architectural Installation Group Co., Ltd.
3-1 Fengyang Road, Longfeng District, Daqing 163711, China
With the rapid development of networked video surveillance systems, human detection is essential. These tasks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, such as security and surveillance. This review paper extensively surveys the current progress made toward human detection in intelligent video surveillance. The algorithms presented in this paper are classified as either human detection without classifier training or human detection with classifier training. In the core techniques of human detection without classifier training, three critical processing stages are discussed including background subtraction, Gaussian mixture model (GMM) and skin color model. In the core techniques of human detection with classifier training, two main types are mentioned including holistic human detector, and part-based human detector. Our survey aims to address existing problems, challenges and future research directions based on the analyses of the current progress made toward human detection techniques in computer vision.
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