Fast Human Detection for Intelligent Monitoring Using Surveillance Visible Sensors

Sensors

Byoung Chul Ko¹  Mira Jeong² 

[1] Department of Computer Engineering, Keimyung University, Sindang-dong, Dalseo-gu, Daegu 704-701, Korea;

关键词: human detection;    Hough windows map;    adaptive ROI;    divide-and-conquer;    CaRF;   

DOI  :  10.3390/s141121247

来源: mdpi

【 摘 要 】

Human detection using visible surveillance sensors is an important and challenging work for intruder detection and safety management. The biggest barrier of real-time human detection is the computational time required for dense image scaling and scanning windows extracted from an entire image. This paper proposes fast human detection by selecting optimal levels of image scale using each level's adaptive region-of-interest (ROI). To estimate the image-scaling level, we generate a Hough windows map (HWM) and select a few optimal image scales based on the strength of the HWM and the divide-and-conquer algorithm. Furthermore, adaptive ROIs are arranged per image scale to provide a different search area. We employ a cascade random forests classifier to separate candidate windows into human and nonhuman classes. The proposed algorithm has been successfully applied to real-world surveillance video sequences, and its detection accuracy and computational speed show a better performance than those of other related methods.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.